#RUN THIS CELL
import requests
from IPython.core.display import HTML
styles = requests.get("https://raw.githubusercontent.com/Harvard-IACS/2018-CS109A/master/content/styles/cs109.css").text
HTML(styles)
import numpy as np
import pandas as pd
import datetime
import warnings
warnings.filterwarnings('ignore')
import statsmodels.api as sm
from statsmodels.api import OLS
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegressionCV
from sklearn.linear_model import LassoCV
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.preprocessing import PolynomialFeatures
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import KFold
from sklearn.preprocessing import MinMaxScaler
from sklearn.utils import resample
# Plotly visualizations
from plotly import tools
import plotly.plotly as py
import plotly.figure_factory as ff
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import tensorflow as tf
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
from sklearn.pipeline import make_pipeline
from sklearn.datasets import make_blobs
import sklearn.metrics as metrics
from sklearn.model_selection import cross_val_score
from sklearn.metrics import accuracy_score
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.linear_model import LogisticRegressionCV
from sklearn.model_selection import KFold
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import StandardScaler
import time
import math
from scipy.special import gamma
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
%matplotlib inline
import seaborn as sns
sns.set()
import matplotlib.style
matplotlib.style.use('seaborn-whitegrid')
sns.set_style("white")
from IPython.display import display
init_notebook_mode(connected=True)
This notebook uses the cleaned CSV data file data_cleaned_2016_2017.csv downloaded from https://drive.google.com/open?id=1LCk-dDFC7O_6ek1i0IIGqE07Rq-kf1Xz.
The cleaned dataset still needs some pre-processing in order to make it ready for modelling. This includes:
# increase some display options to display all columns and more rows.
pd.set_option('display.max_columns', None)
pd.options.display.max_rows = 150
# read in the 2016-2017 data set
original_df = pd.read_csv('../../../data/data_cleaned_2016_2017.csv', low_memory = False)
display(original_df.shape)
original_df.head()
(334109, 89)
| id | loan_amnt | funded_amnt | funded_amnt_inv | term | int_rate | installment | grade | sub_grade | emp_length | home_ownership | annual_inc | verification_status | issue_d | loan_status | pymnt_plan | purpose | zip_code | addr_state | dti | delinq_2yrs | earliest_cr_line | fico_range_low | fico_range_high | inq_last_6mths | mths_since_last_delinq | mths_since_last_record | open_acc | pub_rec | revol_bal | revol_util | total_acc | initial_list_status | mths_since_last_major_derog | application_type | annual_inc_joint | dti_joint | acc_now_delinq | open_acc_6m | open_act_il | open_il_12m | open_il_24m | mths_since_rcnt_il | total_bal_il | il_util | open_rv_12m | open_rv_24m | max_bal_bc | all_util | inq_fi | total_cu_tl | inq_last_12m | acc_open_past_24mths | avg_cur_bal | bc_open_to_buy | bc_util | chargeoff_within_12_mths | delinq_amnt | mo_sin_old_il_acct | mo_sin_old_rev_tl_op | mo_sin_rcnt_rev_tl_op | mo_sin_rcnt_tl | mort_acc | mths_since_recent_bc | mths_since_recent_bc_dlq | mths_since_recent_inq | mths_since_recent_revol_delinq | num_accts_ever_120_pd | num_actv_bc_tl | num_actv_rev_tl | num_bc_sats | num_bc_tl | num_il_tl | num_op_rev_tl | num_rev_accts | num_rev_tl_bal_gt_0 | num_sats | num_tl_120dpd_2m | num_tl_30dpd | num_tl_90g_dpd_24m | num_tl_op_past_12m | pct_tl_nvr_dlq | percent_bc_gt_75 | pub_rec_bankruptcies | tax_liens | revol_bal_joint | sec_app_mort_acc | sec_app_revol_util | sec_app_mths_since_last_major_derog | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 102675947 | 14000.0 | 14000.0 | 14000.0 | 1 | 15.99 | 340.38 | C | C5 | 10.0 | RENT | 43000.0 | Source Verified | 2017 | Charged Off | 0 | debt_consolidation | 367xx | AL | 21.80 | 1.0 | 1995 | 670.0 | 674.0 | 0.0 | 1 | 0 | 3.0 | 0.0 | 18537.0 | 99.1 | 8.0 | 1 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1 | 8035.0 | 42.0 | 0.0 | 0.0 | 18537.0 | 70.0 | 0.0 | 0.0 | 0.0 | 0.0 | 8857.0 | 163.0 | 99.1 | 0.0 | 0.0 | 1 | 1 | 1 | 1 | 0.0 | 1 | 0 | 0 | 0 | 0.0 | 1.0 | 1.0 | 1.0 | 2.0 | 5.0 | 1.0 | 3.0 | 1.0 | 3.0 | 0.0 | 0.0 | 0.0 | 0.0 | 87.5 | 100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 1 | 104220223 | 5000.0 | 5000.0 | 5000.0 | 0 | 14.99 | 173.31 | C | C4 | 10.0 | RENT | 68000.0 | Not Verified | 2017 | Fully Paid | 0 | debt_consolidation | 945xx | CA | 22.50 | 0.0 | 2003 | 660.0 | 664.0 | 0.0 | 1 | 0 | 6.0 | 0.0 | 10276.0 | 90.1 | 18.0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 2.0 | 1 | 25892.0 | 64.0 | 0.0 | 0.0 | 4261.0 | 69.0 | 1.0 | 1.0 | 0.0 | 2.0 | 6028.0 | 1124.0 | 90.1 | 0.0 | 0.0 | 1 | 1 | 1 | 1 | 0.0 | 1 | 0 | 1 | 0 | 0.0 | 4.0 | 4.0 | 4.0 | 6.0 | 8.0 | 4.0 | 9.0 | 4.0 | 6.0 | 0.0 | 0.0 | 0.0 | 0.0 | 94.4 | 75.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 2 | 104090118 | 10150.0 | 10150.0 | 10150.0 | 0 | 7.24 | 314.52 | A | A3 | 8.0 | MORTGAGE | 50000.0 | Not Verified | 2017 | Fully Paid | 0 | debt_consolidation | 773xx | TX | 29.60 | 0.0 | 2002 | 740.0 | 744.0 | 1.0 | 0 | 0 | 9.0 | 0.0 | 21845.0 | 56.0 | 21.0 | 1 | 0 | 0 | 0.0 | 0.0 | 0.0 | 1.0 | 3.0 | 1.0 | 1.0 | 1 | 23502.0 | 43.0 | 0.0 | 0.0 | 11270.0 | 49.0 | 1.0 | 1.0 | 2.0 | 2.0 | 29908.0 | 13951.0 | 58.2 | 0.0 | 0.0 | 1 | 1 | 1 | 1 | 3.0 | 1 | 0 | 1 | 0 | 0.0 | 3.0 | 4.0 | 3.0 | 5.0 | 8.0 | 5.0 | 10.0 | 4.0 | 9.0 | 0.0 | 0.0 | 0.0 | 2.0 | 100.0 | 33.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 3 | 103739310 | 8400.0 | 8400.0 | 8400.0 | 0 | 11.39 | 276.56 | B | B3 | 8.0 | MORTGAGE | 50000.0 | Source Verified | 2017 | Charged Off | 0 | other | 454xx | OH | 15.63 | 0.0 | 2005 | 675.0 | 679.0 | 0.0 | 0 | 0 | 14.0 | 0.0 | 12831.0 | 30.3 | 30.0 | 1 | 0 | 0 | 0.0 | 0.0 | 0.0 | 3.0 | 2.0 | 1.0 | 2.0 | 1 | 38760.0 | 105.0 | 4.0 | 8.0 | 5338.0 | 65.0 | 4.0 | 1.0 | 7.0 | 10.0 | 12389.0 | 24145.0 | 33.1 | 0.0 | 0.0 | 1 | 1 | 1 | 1 | 4.0 | 1 | 0 | 1 | 0 | 0.0 | 4.0 | 5.0 | 7.0 | 11.0 | 9.0 | 11.0 | 16.0 | 5.0 | 14.0 | 0.0 | 0.0 | 0.0 | 5.0 | 100.0 | 14.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
| 4 | 104046532 | 10000.0 | 10000.0 | 10000.0 | 0 | 12.74 | 335.69 | C | C1 | 10.0 | OWN | 40000.0 | Not Verified | 2017 | Fully Paid | 0 | debt_consolidation | 324xx | FL | 8.85 | 0.0 | 1997 | 700.0 | 704.0 | 0.0 | 0 | 0 | 7.0 | 0.0 | 9227.0 | 55.9 | 15.0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 | 0.0 | 0.0 | 1.0 | 3.0 | 5454.0 | 56.0 | 0.0 | 0.0 | 1.0 | 3.0 | 1318.0 | 1691.0 | 79.4 | 0.0 | 0.0 | 1 | 1 | 1 | 1 | 2.0 | 1 | 0 | 1 | 0 | 0.0 | 2.0 | 4.0 | 2.0 | 3.0 | 2.0 | 7.0 | 11.0 | 4.0 | 7.0 | 0.0 | 0.0 | 0.0 | 1.0 | 100.0 | 50.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 |
We now use the pd.DataFrame.corr() function to find correlations between variables. A value of 1.0 means the variables are perfectly correlated and a value of 0 means they're not correlated at all. We decided on a threshold value of $> 0.9$.
correlations = original_df.corr()
threshold = 0.9
# display entire matrix and color in red if predictors > threshold
correlations.style.apply(lambda x: ["background: red" if v > threshold else "" for v in x], axis = 1)
| id | loan_amnt | funded_amnt | funded_amnt_inv | term | int_rate | installment | emp_length | annual_inc | issue_d | pymnt_plan | dti | delinq_2yrs | earliest_cr_line | fico_range_low | fico_range_high | inq_last_6mths | mths_since_last_delinq | mths_since_last_record | open_acc | pub_rec | revol_bal | revol_util | total_acc | initial_list_status | mths_since_last_major_derog | application_type | annual_inc_joint | dti_joint | acc_now_delinq | open_acc_6m | open_act_il | open_il_12m | open_il_24m | mths_since_rcnt_il | total_bal_il | il_util | open_rv_12m | open_rv_24m | max_bal_bc | all_util | inq_fi | total_cu_tl | inq_last_12m | acc_open_past_24mths | avg_cur_bal | bc_open_to_buy | bc_util | chargeoff_within_12_mths | delinq_amnt | mo_sin_old_il_acct | mo_sin_old_rev_tl_op | mo_sin_rcnt_rev_tl_op | mo_sin_rcnt_tl | mort_acc | mths_since_recent_bc | mths_since_recent_bc_dlq | mths_since_recent_inq | mths_since_recent_revol_delinq | num_accts_ever_120_pd | num_actv_bc_tl | num_actv_rev_tl | num_bc_sats | num_bc_tl | num_il_tl | num_op_rev_tl | num_rev_accts | num_rev_tl_bal_gt_0 | num_sats | num_tl_120dpd_2m | num_tl_30dpd | num_tl_90g_dpd_24m | num_tl_op_past_12m | pct_tl_nvr_dlq | percent_bc_gt_75 | pub_rec_bankruptcies | tax_liens | revol_bal_joint | sec_app_mort_acc | sec_app_revol_util | sec_app_mths_since_last_major_derog | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | 1 | -0.0456292 | -0.0456292 | -0.045516 | 0.00510278 | 0.0848256 | -0.0383872 | -0.0169376 | 0.00244029 | 0.833589 | nan | -0.0169884 | -0.0127991 | 0.0845452 | 0.068197 | 0.0681967 | -0.00241664 | -0.0189844 | -0.00713965 | -0.0315555 | -0.0184074 | -0.0311083 | -0.0859639 | -0.0401273 | -0.039018 | -0.016988 | 0.146709 | 0.138757 | 0.138557 | -0.0130889 | -0.0192934 | -0.00926293 | -0.0218833 | 0.00589419 | -0.00312323 | 0.0045272 | -0.0244249 | -0.0139552 | -0.0177426 | -0.0219051 | -0.0811412 | 0.0338149 | 0.00451937 | -0.00235927 | -0.00409831 | 0.027256 | 0.0610435 | -0.091366 | -0.000865381 | -0.00363466 | nan | nan | nan | nan | -0.0277226 | -0.0151451 | -0.0203849 | 0.00170865 | -0.0187132 | -0.00189675 | -0.0346523 | -0.0549845 | -0.0103156 | -0.0429805 | -0.00498769 | -0.0274384 | -0.0466677 | -0.0602487 | -0.0309258 | -0.00595114 | -0.0102585 | -0.00561178 | -0.0179269 | -0.00156829 | -0.0760892 | 0.00648718 | -0.0176822 | 0.173537 | 0.150042 | 0.201232 | 0.134191 |
| loan_amnt | -0.0456292 | 1 | 1 | 0.999993 | 0.371943 | 0.171388 | 0.955203 | 0.100803 | 0.290591 | -0.0223079 | nan | 0.0354099 | -0.00144835 | -0.138749 | 0.0734488 | 0.0734488 | -0.00949889 | -0.00402864 | -0.067105 | 0.17634 | -0.0470971 | 0.300435 | 0.119665 | 0.190422 | 0.0639881 | -0.0380772 | 0.10254 | 0.128165 | 0.0989231 | 0.0017271 | -0.0153681 | 0.0304857 | 0.00200697 | 0.0315075 | 0.0499328 | 0.157087 | -0.0383439 | -0.034697 | -0.017261 | 0.353818 | 0.0146875 | 0.00764786 | 0.0695528 | 0.0164975 | 0.019626 | 0.215998 | 0.188939 | 0.0761079 | -0.00195514 | 0.00276253 | nan | nan | nan | nan | 0.212504 | 0.0462264 | -0.0068254 | 0.0138877 | -0.00389924 | -0.043254 | 0.199607 | 0.161251 | 0.220138 | 0.199425 | 0.0729088 | 0.160412 | 0.163052 | 0.157129 | 0.176213 | 0.0013363 | 0.00312459 | -0.0185283 | -0.012524 | 0.0702641 | 0.04447 | -0.0722752 | 0.0132666 | 0.0917166 | 0.0654644 | 0.0651616 | 0.025622 |
| funded_amnt | -0.0456292 | 1 | 1 | 0.999993 | 0.371943 | 0.171388 | 0.955203 | 0.100803 | 0.290591 | -0.0223079 | nan | 0.0354099 | -0.00144835 | -0.138749 | 0.0734488 | 0.0734488 | -0.00949889 | -0.00402864 | -0.067105 | 0.17634 | -0.0470971 | 0.300435 | 0.119665 | 0.190422 | 0.0639881 | -0.0380772 | 0.10254 | 0.128165 | 0.0989231 | 0.0017271 | -0.0153681 | 0.0304857 | 0.00200697 | 0.0315075 | 0.0499328 | 0.157087 | -0.0383439 | -0.034697 | -0.017261 | 0.353818 | 0.0146875 | 0.00764786 | 0.0695528 | 0.0164975 | 0.019626 | 0.215998 | 0.188939 | 0.0761079 | -0.00195514 | 0.00276253 | nan | nan | nan | nan | 0.212504 | 0.0462264 | -0.0068254 | 0.0138877 | -0.00389924 | -0.043254 | 0.199607 | 0.161251 | 0.220138 | 0.199425 | 0.0729088 | 0.160412 | 0.163052 | 0.157129 | 0.176213 | 0.0013363 | 0.00312459 | -0.0185283 | -0.012524 | 0.0702641 | 0.04447 | -0.0722752 | 0.0132666 | 0.0917166 | 0.0654644 | 0.0651616 | 0.025622 |
| funded_amnt_inv | -0.045516 | 0.999993 | 0.999993 | 1 | 0.372176 | 0.171397 | 0.955109 | 0.100855 | 0.290609 | -0.0221908 | nan | 0.0353379 | -0.00149396 | -0.138754 | 0.0735679 | 0.0735679 | -0.0095706 | -0.00407488 | -0.0671567 | 0.176311 | -0.0471432 | 0.300432 | 0.119642 | 0.190395 | 0.0647964 | -0.0381205 | 0.102532 | 0.12816 | 0.0989095 | 0.00170044 | -0.0154125 | 0.0304666 | 0.00198669 | 0.0314859 | 0.0499276 | 0.157077 | -0.0383544 | -0.0347373 | -0.0173085 | 0.353833 | 0.0146675 | 0.00763789 | 0.0695417 | 0.0164552 | 0.0195828 | 0.216055 | 0.188967 | 0.0760853 | -0.00196807 | 0.00274976 | nan | nan | nan | nan | 0.212543 | 0.0462363 | -0.00685981 | 0.0138697 | -0.00393152 | -0.0432889 | 0.199572 | 0.161207 | 0.220116 | 0.199397 | 0.0728883 | 0.160384 | 0.163024 | 0.157086 | 0.176186 | 0.00131546 | 0.00310217 | -0.018561 | -0.0125626 | 0.0703008 | 0.0444573 | -0.0723112 | 0.0132395 | 0.0917409 | 0.0654809 | 0.065188 | 0.0256408 |
| term | 0.00510278 | 0.371943 | 0.371943 | 0.372176 | 1 | 0.394674 | 0.14862 | 0.0600385 | 0.0547661 | 0.0123301 | nan | 0.0483536 | -0.0106056 | -0.0360779 | 0.00229987 | 0.00230004 | 0.00795103 | -0.0100385 | -0.0059825 | 0.0711056 | -0.0108748 | 0.0722922 | 0.0578313 | 0.0880162 | 0.176625 | -0.0140784 | 0.0642611 | 0.0643084 | 0.0637183 | -0.00242646 | 0.0139953 | 0.0343187 | 0.0419519 | 0.0627368 | 0.0352736 | 0.0769668 | 0.042335 | -0.0146462 | -0.00613441 | 0.0903663 | 0.050348 | 0.027585 | 0.0538909 | 0.0303021 | 0.0368651 | 0.0757197 | 0.0209426 | 0.0446585 | -0.00225203 | 0.000162738 | nan | nan | nan | nan | 0.0901313 | 0.0152259 | -0.00936181 | 0.0248917 | -0.0117617 | -0.0171136 | 0.0527023 | 0.0559652 | 0.0602925 | 0.0500086 | 0.0653109 | 0.0507575 | 0.0501363 | 0.0528589 | 0.0707827 | -0.00132722 | -0.00163741 | -0.0113272 | 0.0181777 | 0.0383303 | 0.0400646 | -0.0025567 | -0.00782063 | 0.0546446 | 0.0462316 | 0.0512744 | 0.0269252 |
| int_rate | 0.0848256 | 0.171388 | 0.171388 | 0.171397 | 0.394674 | 1 | 0.214653 | -0.0163869 | -0.0710463 | 0.059859 | nan | 0.158991 | 0.0322546 | 0.119122 | -0.358621 | -0.358618 | 0.199015 | 0.0460827 | 0.0646569 | 0.0110733 | 0.0548128 | -0.0107056 | 0.212328 | -0.0444001 | -0.162455 | 0.0596923 | 0.0411864 | 0.0263215 | 0.0560887 | 0.0065543 | 0.170999 | 0.0333187 | 0.19403 | 0.171914 | -0.0102362 | 0.0376085 | 0.125128 | 0.150425 | 0.158809 | -0.0424092 | 0.25345 | 0.145232 | 0.0204437 | 0.1967 | 0.208535 | -0.0883193 | -0.244328 | 0.210012 | 0.00647974 | 0.00431558 | nan | nan | nan | nan | -0.111063 | -0.0289126 | 0.0239265 | 0.095931 | 0.0266176 | 0.0362125 | 0.0534045 | 0.0947076 | -0.0243449 | -0.0745048 | 0.0154559 | 0.00648551 | -0.0537557 | 0.0955494 | 0.0108543 | 0.00235276 | 0.00567208 | 0.0198795 | 0.214301 | -0.0472754 | 0.205924 | 0.0569179 | 0.0151899 | 0.0305633 | 0.00964099 | 0.0466987 | 0.0322549 |
| installment | -0.0383872 | 0.955203 | 0.955203 | 0.955109 | 0.14862 | 0.214653 | 1 | 0.0877734 | 0.272842 | -0.0204832 | nan | 0.0473371 | 0.0060309 | -0.115743 | 0.0210835 | 0.0210838 | 0.0163271 | 0.00647653 | -0.0553965 | 0.167188 | -0.0355255 | 0.28601 | 0.139345 | 0.16772 | -0.00837457 | -0.0264476 | 0.0913125 | 0.113794 | 0.0907054 | 0.00338448 | 0.00464014 | 0.0273263 | 0.0181085 | 0.0411935 | 0.0420945 | 0.146332 | -0.0330059 | -0.00946319 | 0.00898484 | 0.331862 | 0.0367535 | 0.0209666 | 0.0597467 | 0.037195 | 0.0424672 | 0.184681 | 0.150411 | 0.098197 | -0.000521834 | 0.00357904 | nan | nan | nan | nan | 0.175254 | 0.0421676 | -0.000387367 | 0.0207774 | 0.00415115 | -0.0346932 | 0.20317 | 0.168627 | 0.209793 | 0.184321 | 0.0598414 | 0.156451 | 0.149827 | 0.165577 | 0.16713 | 0.00192625 | 0.00431939 | -0.0139407 | 0.0132499 | 0.0553822 | 0.0651471 | -0.0633205 | 0.018998 | 0.0794495 | 0.0522894 | 0.0577813 | 0.0232078 |
| emp_length | -0.0169376 | 0.100803 | 0.100803 | 0.100855 | 0.0600385 | -0.0163869 | 0.0877734 | 1 | 0.0991428 | -0.0165807 | nan | -0.0122115 | 0.0192935 | -0.115603 | 0.00891098 | 0.00891115 | 0.0031631 | 0.0456234 | 0.0160075 | 0.0668743 | 0.0123518 | 0.0870979 | 0.0509222 | 0.103768 | 0.0200416 | 0.0245972 | -0.0747383 | -0.0584512 | -0.0694189 | 0.0108487 | 0.0226559 | -0.0626386 | 0.0474697 | 0.0610861 | 0.0523735 | -0.00350992 | -0.019046 | 0.00905316 | 0.00405258 | 0.0713191 | -0.0147168 | 0.0092254 | 0.0834455 | 0.000861288 | 0.038457 | 0.0955353 | 0.0212737 | 0.0420823 | 0.00673721 | 0.00203488 | nan | nan | nan | nan | 0.158505 | 0.00463817 | 0.0261163 | 0.00435118 | 0.030035 | 0.00493574 | 0.0758387 | 0.111782 | 0.0688841 | 0.0899515 | 0.00275573 | 0.0982971 | 0.11622 | 0.112049 | 0.0659711 | 0.00146513 | 0.0105402 | -0.00294973 | 0.0352015 | -0.0166497 | 0.0374231 | 0.00503536 | 0.00531877 | -0.0640999 | -0.0570702 | -0.0825761 | -0.0539475 |
| annual_inc | 0.00244029 | 0.290591 | 0.290591 | 0.290609 | 0.0547661 | -0.0710463 | 0.272842 | 0.0991428 | 1 | 0.00654048 | nan | -0.126809 | 0.0282487 | -0.119002 | 0.058931 | 0.0589308 | 0.0292207 | 0.0405467 | -0.0244687 | 0.126109 | -0.00382472 | 0.272479 | 0.0446215 | 0.15851 | 0.0346802 | 0.0116384 | -0.0536792 | -0.0215331 | -0.0542628 | 0.0128571 | 0.0373415 | 0.0633142 | 0.0797827 | 0.102467 | 0.0499242 | 0.201533 | 0.00228085 | -0.00400232 | -0.0103785 | 0.24945 | 0.0067374 | 0.053162 | 0.0419613 | 0.064537 | 0.0610748 | 0.287459 | 0.153362 | 0.0121136 | 0.00481134 | 0.00818791 | nan | nan | nan | nan | 0.198136 | 0.0176033 | 0.0295602 | 0.0316428 | 0.0280972 | 0.00959516 | 0.106956 | 0.0792329 | 0.123213 | 0.128081 | 0.0929922 | 0.0782448 | 0.0984909 | 0.077653 | 0.125925 | 0.00766741 | 0.0105974 | 0.00193351 | 0.0520409 | 0.000633011 | 0.000613847 | -0.0367303 | 0.0350064 | -0.0211773 | -0.0262688 | -0.0391232 | -0.0272948 |
| issue_d | 0.833589 | -0.0223079 | -0.0223079 | -0.0221908 | 0.0123301 | 0.059859 | -0.0204832 | -0.0165807 | 0.00654048 | 1 | nan | -0.00674063 | -0.0144732 | 0.0647172 | 0.0604769 | 0.0604766 | -0.00617351 | -0.0207553 | -0.0081671 | -0.0167096 | -0.0157999 | -0.0115209 | -0.066062 | -0.0251697 | -0.0257494 | -0.0190358 | 0.131558 | 0.123699 | 0.12324 | -0.00929717 | -0.0237732 | -0.0104031 | -0.0312628 | -0.00431884 | -0.00258569 | 0.0028623 | -0.0295579 | -0.0136302 | -0.0139293 | -0.0016616 | -0.0746282 | 0.0260245 | 0.00432069 | -0.00388903 | -0.00624408 | 0.0272625 | 0.0629564 | -0.0728878 | 0.00153744 | -0.00383244 | nan | nan | nan | nan | -0.0154189 | -0.00796961 | -0.0223056 | 0.000616289 | -0.0197745 | -0.00689764 | -0.0152928 | -0.032089 | 0.00675165 | -0.0233043 | -0.00598991 | -0.00992501 | -0.0268819 | -0.0363046 | -0.0163005 | -0.00344101 | -0.00769706 | -0.00695552 | -0.0211688 | 0.00721334 | -0.0597402 | 0.0029982 | -0.0132262 | 0.153795 | 0.135667 | 0.181264 | 0.120069 |
| pymnt_plan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| dti | -0.0169884 | 0.0354099 | 0.0354099 | 0.0353379 | 0.0483536 | 0.158991 | 0.0473371 | -0.0122115 | -0.126809 | -0.00674063 | nan | 1 | -0.0104998 | -0.0338418 | -0.0540789 | -0.0540823 | 0.00111515 | -0.0170658 | -0.0245811 | 0.195946 | -0.0305627 | 0.103301 | 0.148754 | 0.160071 | -0.0259364 | -0.0273196 | 0.189708 | 0.167284 | 0.231641 | -0.00105407 | 0.0302801 | 0.173504 | 0.131945 | 0.180339 | 0.11519 | 0.192938 | 0.165749 | -0.000557731 | 0.0239507 | 0.0851506 | 0.170675 | 0.055609 | 0.09578 | 0.0308119 | 0.101628 | -0.0724721 | -0.063867 | 0.150757 | -0.00259618 | -0.00724449 | nan | nan | nan | nan | -0.0128473 | 0.00830909 | -0.0249932 | 0.0291981 | -0.0196061 | -0.0274789 | 0.126229 | 0.181384 | 0.0754692 | 0.0552756 | 0.157749 | 0.125151 | 0.097101 | 0.184929 | 0.195886 | -0.00494301 | 0.001442 | -0.0118116 | 0.0566104 | 0.0640695 | 0.133691 | -0.0127161 | -0.0257022 | 0.157884 | 0.12818 | 0.156272 | 0.0887798 |
| delinq_2yrs | -0.0127991 | -0.00144835 | -0.00144835 | -0.00149396 | -0.0106056 | 0.0322546 | 0.0060309 | 0.0192935 | 0.0282487 | -0.0144732 | nan | -0.0104998 | 1 | -0.0749717 | -0.171801 | -0.171798 | 0.0269384 | 0.344679 | -0.0506505 | 0.0522946 | -0.0366986 | -0.0226474 | -0.00458231 | 0.112875 | -0.011731 | 0.226492 | -0.00262258 | 0.00268839 | -0.00373691 | 0.123617 | -0.000239808 | 0.0640688 | -0.00885671 | -0.0258594 | 0.0277418 | 0.0485201 | 0.0141296 | -0.0234147 | -0.0489541 | -0.0515156 | 0.0213504 | 0.0229035 | 0.0172532 | 0.0264162 | -0.0585419 | 0.0317852 | -0.061073 | -0.00470567 | 0.152355 | 0.035054 | nan | nan | nan | nan | 0.0571907 | -0.0158934 | 0.317727 | 0.0128847 | 0.352637 | 0.216559 | -0.0328287 | 0.00268074 | -0.026932 | 0.0308758 | 0.082474 | 0.0110338 | 0.0751926 | -0.00182585 | 0.049985 | 0.0472085 | 0.103201 | 0.665355 | -0.0285821 | -0.440008 | -0.00673088 | -0.064268 | 0.00593368 | -0.00670418 | -0.00149503 | -0.00247188 | 0.0117696 |
| earliest_cr_line | 0.0845452 | -0.138749 | -0.138749 | -0.138754 | -0.0360779 | 0.119122 | -0.115743 | -0.115603 | -0.119002 | 0.0647172 | nan | -0.0338418 | -0.0749717 | 1 | -0.090225 | -0.0902306 | -0.00866513 | -0.132666 | -0.0682431 | -0.137432 | -0.059932 | -0.200211 | -0.0336224 | -0.265802 | -0.0406771 | -0.112574 | 0.0119714 | -0.00187587 | 0.00987256 | -0.0314438 | -0.0118584 | 0.0583278 | -0.00247454 | 0.00875091 | -0.036872 | -0.0285505 | 0.0601059 | 0.00055375 | 0.024354 | -0.190607 | 0.0536393 | 0.0292811 | -0.0403152 | 0.0223407 | 0.021144 | -0.118321 | -0.140977 | -0.0234079 | -0.0266618 | -0.00867312 | nan | nan | nan | nan | -0.27796 | -0.00855518 | -0.120467 | 0.019383 | -0.132552 | -0.0854754 | -0.116417 | -0.15247 | -0.13836 | -0.263238 | -0.0181363 | -0.169198 | -0.307887 | -0.149252 | -0.133176 | -0.0106247 | -0.0273597 | -0.0329759 | -0.00379716 | 0.0884751 | -0.0353742 | -0.0560401 | -0.0281559 | -0.00784146 | -0.0099418 | 0.0119592 | 0.0037996 |
| fico_range_low | 0.068197 | 0.0734488 | 0.0734488 | 0.0735679 | 0.00229987 | -0.358621 | 0.0210835 | 0.00891098 | 0.058931 | 0.0604769 | nan | -0.0540789 | -0.171801 | -0.090225 | 1 | 1 | -0.097505 | -0.307014 | -0.243953 | 0.0196743 | -0.20027 | -0.000530307 | -0.465726 | 0.0252076 | 0.0770303 | -0.282168 | 0.0178947 | 0.0257033 | 0.010836 | -0.0428563 | -0.0616149 | -0.0134152 | -0.0216399 | -0.017437 | -0.00255944 | 0.0117294 | -0.0818219 | -0.109483 | -0.133455 | 0.0460722 | -0.416169 | -0.0815677 | -0.00267085 | -0.126787 | -0.10921 | 0.10444 | 0.507928 | -0.470744 | -0.0528395 | -0.0151574 | nan | nan | nan | nan | 0.104666 | 0.00784537 | -0.202268 | -0.094217 | -0.236348 | -0.194331 | -0.120806 | -0.19519 | 0.0642446 | 0.0737801 | -0.0171835 | 0.0134573 | 0.0295203 | -0.195455 | 0.0240883 | -0.0180953 | -0.0321727 | -0.101024 | -0.0953209 | 0.296871 | -0.399495 | -0.206976 | -0.0617305 | 0.0357951 | 0.0349613 | 0.0193274 | 0.00648239 |
| fico_range_high | 0.0681967 | 0.0734488 | 0.0734488 | 0.0735679 | 0.00230004 | -0.358618 | 0.0210838 | 0.00891115 | 0.0589308 | 0.0604766 | nan | -0.0540823 | -0.171798 | -0.0902306 | 1 | 1 | -0.0975053 | -0.307012 | -0.243949 | 0.0196713 | -0.200267 | -0.000532927 | -0.465723 | 0.0252047 | 0.0770285 | -0.282164 | 0.0178956 | 0.025704 | 0.0108358 | -0.0428545 | -0.0616173 | -0.0134182 | -0.0216427 | -0.0174411 | -0.00256083 | 0.0117265 | -0.0818255 | -0.109485 | -0.133456 | 0.0460666 | -0.41617 | -0.0815686 | -0.00267086 | -0.126788 | -0.109214 | 0.104442 | 0.507929 | -0.470741 | -0.052837 | -0.0151572 | nan | nan | nan | nan | 0.104667 | 0.00784578 | -0.202266 | -0.0942215 | -0.236346 | -0.194328 | -0.120807 | -0.19519 | 0.0642422 | 0.0737779 | -0.0171868 | 0.0134556 | 0.0295188 | -0.195455 | 0.0240852 | -0.018095 | -0.0321722 | -0.101022 | -0.0953241 | 0.296867 | -0.399491 | -0.206973 | -0.0617298 | 0.0357942 | 0.0349624 | 0.0193286 | 0.00648387 |
| inq_last_6mths | -0.00241664 | -0.00949889 | -0.00949889 | -0.0095706 | 0.00795103 | 0.199015 | 0.0163271 | 0.0031631 | 0.0292207 | -0.00617351 | nan | 0.00111515 | 0.0269384 | -0.00866513 | -0.097505 | -0.0975053 | 1 | 0.0402042 | 0.0876163 | 0.167398 | 0.0750018 | -0.00286105 | -0.102894 | 0.160403 | -0.0554694 | 0.0821566 | -0.0223655 | -0.0208437 | -0.0196721 | -0.00414494 | 0.424702 | 0.0306383 | 0.145311 | 0.117973 | 0.0198792 | 0.0504319 | 0.0717362 | 0.341016 | 0.298656 | -0.0542851 | -0.048355 | 0.218088 | 0.0313672 | 0.484791 | 0.303038 | -0.0440917 | 0.0318196 | -0.0897593 | 0.0102862 | -0.00224899 | nan | nan | nan | nan | 0.0126668 | 0.0173994 | 0.0349001 | 0.209879 | 0.0338875 | 0.0462168 | 0.101559 | 0.156783 | 0.15623 | 0.141211 | 0.0668369 | 0.175681 | 0.172687 | 0.129672 | 0.163325 | -0.00281417 | -0.00666443 | 0.0277039 | 0.353596 | -0.022165 | -0.0854127 | 0.0855617 | 0.0181919 | -0.0193547 | -0.0154953 | -0.021927 | -0.00925314 |
| mths_since_last_delinq | -0.0189844 | -0.00402864 | -0.00402864 | -0.00407488 | -0.0100385 | 0.0460827 | 0.00647653 | 0.0456234 | 0.0405467 | -0.0207553 | nan | -0.0170658 | 0.344679 | -0.132666 | -0.307014 | -0.307012 | 0.0402042 | 1 | -0.0467039 | 0.0563469 | -0.0189539 | -0.0471993 | 0.0101965 | 0.164877 | -0.00998736 | 0.570468 | -0.00666193 | -0.000829696 | -0.0068887 | 0.0689108 | 0.0491603 | 0.0527387 | 0.0388924 | 0.0373714 | 0.0681927 | 0.0643094 | 0.0429723 | 0.0417107 | 0.0353053 | -0.0817066 | 0.053505 | 0.073993 | 0.0386346 | 0.074587 | 0.047586 | 0.0490007 | -0.111267 | 0.0182409 | 0.0702054 | 0.0207741 | nan | nan | nan | nan | 0.107099 | -0.0147326 | 0.550657 | 0.0537591 | 0.707576 | 0.362446 | -0.0361648 | 0.0130495 | -0.0490637 | 0.0499351 | 0.120277 | 0.0219983 | 0.103071 | 0.0165125 | 0.0554331 | 0.0287721 | 0.0562023 | 0.152675 | 0.0539345 | -0.608835 | 0.0043547 | -0.0888289 | 0.0265877 | -0.0151579 | -0.00237543 | -0.00673003 | 0.0257454 |
| mths_since_last_record | -0.00713965 | -0.067105 | -0.067105 | -0.0671567 | -0.0059825 | 0.0646569 | -0.0553965 | 0.0160075 | -0.0244687 | -0.0081671 | nan | -0.0245811 | -0.0506505 | -0.0682431 | -0.243953 | -0.243949 | 0.0876163 | -0.0467039 | 1 | -0.0119334 | 0.796918 | -0.11151 | -0.0671207 | 0.010897 | -0.0179055 | -0.000451319 | -0.000572396 | -0.00698251 | -0.00275022 | -0.0054401 | 0.0661928 | -0.0315764 | 0.0486684 | 0.0540555 | 0.0218196 | -0.0264372 | 0.0319805 | 0.093185 | 0.122713 | -0.144157 | -0.0136064 | 0.0762482 | 0.00847277 | 0.105516 | 0.123906 | -0.0736874 | -0.113859 | -0.0168851 | -0.011433 | 0.000426055 | nan | nan | nan | nan | -0.0264072 | 0.00549048 | -0.05204 | 0.0695888 | -0.0663142 | -0.0108138 | -0.0466023 | 0.00624367 | -0.0602441 | -0.0142455 | -0.0135545 | 0.0195522 | 0.035464 | 0.00290667 | -0.0173759 | 0.00103694 | -0.00998001 | -0.0169895 | 0.0987125 | 0.0368363 | -0.0300964 | 0.776127 | 0.299501 | -0.0158114 | -0.00506234 | -0.00774973 | -0.0118531 |
| open_acc | -0.0315555 | 0.17634 | 0.17634 | 0.176311 | 0.0711056 | 0.0110733 | 0.167188 | 0.0668743 | 0.126109 | -0.0167096 | nan | 0.195946 | 0.0522946 | -0.137432 | 0.0196743 | 0.0196713 | 0.167398 | 0.0563469 | -0.0119334 | 1 | -0.015478 | 0.21703 | -0.135009 | 0.711668 | 0.0152771 | 0.0159066 | -0.0184166 | -0.000557158 | -0.000784209 | 0.0224183 | 0.281506 | 0.51498 | 0.167873 | 0.237871 | 0.108818 | 0.342034 | 0.21282 | 0.369619 | 0.473997 | 0.106785 | -0.0123935 | 0.108395 | 0.101001 | 0.184489 | 0.51123 | -0.124814 | 0.305943 | -0.0741913 | 0.005978 | 0.00331224 | nan | nan | nan | nan | 0.128376 | 0.0907996 | 0.0272822 | 0.128645 | 0.0595541 | 0.0314553 | 0.551326 | 0.661589 | 0.6326 | 0.5457 | 0.38191 | 0.840337 | 0.671288 | 0.663984 | 0.998662 | 0.00466632 | 0.0196159 | 0.0159108 | 0.391381 | 0.100041 | -0.0750634 | -0.0134098 | -0.0073593 | 0.0107744 | -0.0037312 | -0.0234721 | -0.0122987 |
| pub_rec | -0.0184074 | -0.0470971 | -0.0470971 | -0.0471432 | -0.0108748 | 0.0548128 | -0.0355255 | 0.0123518 | -0.00382472 | -0.0157999 | nan | -0.0305627 | -0.0366986 | -0.059932 | -0.20027 | -0.200267 | 0.0750018 | -0.0189539 | 0.796918 | -0.015478 | 1 | -0.0872123 | -0.0517418 | -0.00591755 | -0.0174667 | 0.0148058 | -0.00585126 | -0.00959029 | -0.00793729 | -0.00257833 | 0.0568102 | -0.028641 | 0.0374344 | 0.0381032 | 0.0166762 | -0.0223009 | 0.0232084 | 0.0807145 | 0.103701 | -0.114796 | -0.00883367 | 0.0674735 | -0.00157323 | 0.0904722 | 0.0998176 | -0.0579094 | -0.0907413 | -0.0153993 | -0.00895587 | 0.00281559 | nan | nan | nan | nan | -0.0255759 | 0.005651 | -0.0322887 | 0.0573158 | -0.0431191 | 0.00321878 | -0.029659 | 0.00564454 | -0.044667 | -0.0180016 | -0.0178215 | 0.0107301 | 0.0135757 | 0.00396216 | -0.019072 | 0.00475399 | -0.00658831 | -0.0117064 | 0.0823809 | 0.0121686 | -0.0295045 | 0.654163 | 0.686977 | -0.0161148 | -0.0083866 | -0.0103497 | -0.0104793 |
| revol_bal | -0.0311083 | 0.300435 | 0.300435 | 0.300432 | 0.0722922 | -0.0107056 | 0.28601 | 0.0870979 | 0.272479 | -0.0115209 | nan | 0.103301 | -0.0226474 | -0.200211 | -0.000530307 | -0.000532927 | -0.00286105 | -0.0471993 | -0.11151 | 0.21703 | -0.0872123 | 1 | 0.262357 | 0.185866 | 0.0219457 | -0.0746145 | -0.00300515 | 0.0172646 | 0.00648542 | 0.00352412 | -0.0193387 | -0.0012892 | -0.0352578 | -0.0244266 | 0.0215199 | 0.0925043 | -0.0592588 | -0.00142028 | 0.00277935 | 0.546885 | 0.109251 | -0.0468321 | 0.0357439 | -0.0253142 | -0.00365459 | 0.281683 | 0.150496 | 0.191461 | -0.00967216 | 0.00223265 | nan | nan | nan | nan | 0.216294 | 0.0414679 | -0.0326004 | -0.00693511 | -0.0274949 | -0.070373 | 0.309452 | 0.309683 | 0.279112 | 0.232235 | 0.0125949 | 0.226513 | 0.215664 | 0.308051 | 0.215405 | -0.00285345 | 0.0069155 | -0.028822 | -0.0157294 | 0.0965034 | 0.157006 | -0.107666 | -0.010275 | 0.0350645 | 0.00766717 | 0.00317158 | -0.0122944 |
| revol_util | -0.0859639 | 0.119665 | 0.119665 | 0.119642 | 0.0578313 | 0.212328 | 0.139345 | 0.0509222 | 0.0446215 | -0.066062 | nan | 0.148754 | -0.00458231 | -0.0336224 | -0.465726 | -0.465723 | -0.102894 | 0.0101965 | -0.0671207 | -0.135009 | -0.0517418 | 0.262357 | 1 | -0.108596 | -0.0264031 | 0.00484744 | 0.0246415 | 0.0259453 | 0.0342257 | -0.0303393 | -0.181813 | 0.0458067 | -0.0838585 | -0.0664247 | 0.01289 | 0.0328792 | -0.00230849 | -0.197121 | -0.215097 | 0.3258 | 0.65511 | -0.0734558 | 0.0355184 | -0.123023 | -0.209715 | 0.142375 | -0.464889 | 0.841099 | -0.0120491 | -0.00763357 | nan | nan | nan | nan | 0.0337002 | 0.0104042 | -0.00121411 | -0.063428 | -0.00476487 | -0.0197588 | 0.114271 | 0.122157 | -0.11625 | -0.150926 | 0.0158534 | -0.201499 | -0.181344 | 0.130536 | -0.135513 | -0.0146131 | -0.0215178 | -0.00928085 | -0.206963 | -0.0403029 | 0.723753 | -0.0690148 | -0.00643908 | 0.0198315 | 0.00492423 | 0.0295645 | -0.00544781 |
| total_acc | -0.0401273 | 0.190422 | 0.190422 | 0.190395 | 0.0880162 | -0.0444001 | 0.16772 | 0.103768 | 0.15851 | -0.0251697 | nan | 0.160071 | 0.112875 | -0.265802 | 0.0252076 | 0.0252047 | 0.160403 | 0.164877 | 0.010897 | 0.711668 | -0.00591755 | 0.185866 | -0.108596 | 1 | 0.0340599 | 0.12161 | -0.0146581 | 0.00482136 | 0.00074875 | 0.0288889 | 0.258755 | 0.373834 | 0.254223 | 0.348491 | 0.16105 | 0.407291 | 0.185937 | 0.257423 | 0.321715 | 0.121812 | -0.00023709 | 0.16573 | 0.29089 | 0.213623 | 0.455349 | 0.0360446 | 0.244732 | -0.0744188 | 0.0361676 | 0.0043422 | nan | nan | nan | nan | 0.347698 | 0.0360243 | 0.126063 | 0.132395 | 0.153044 | 0.145121 | 0.304611 | 0.404457 | 0.412615 | 0.621164 | 0.69256 | 0.577508 | 0.761235 | 0.405051 | 0.708356 | 0.00882538 | 0.0229275 | 0.0656149 | 0.354305 | 0.0289515 | -0.0646244 | 0.0234767 | -0.0252072 | 0.0101585 | 0.0147019 | -0.020617 | -0.0080504 |
| initial_list_status | -0.039018 | 0.0639881 | 0.0639881 | 0.0647964 | 0.176625 | -0.162455 | -0.00837457 | 0.0200416 | 0.0346802 | -0.0257494 | nan | -0.0259364 | -0.011731 | -0.0406771 | 0.0770303 | 0.0770285 | -0.0554694 | -0.00998736 | -0.0179055 | 0.0152771 | -0.0174667 | 0.0219457 | -0.0264031 | 0.0340599 | 1 | -0.0163854 | 0.0156404 | 0.0196043 | 0.0126395 | -0.000441343 | -0.0436611 | 0.00142055 | -0.0390542 | -0.0274876 | 0.0124691 | 0.0131395 | -0.0197974 | -0.0463635 | -0.044386 | 0.0388421 | -0.0430698 | -0.0301753 | 0.0112963 | -0.0453963 | -0.0452869 | 0.0415878 | 0.0553371 | -0.028589 | -0.000363524 | -0.00318297 | nan | nan | nan | nan | 0.0511063 | 0.0128764 | -0.00611035 | -0.0138002 | -0.00568527 | -0.0122598 | 0.00199673 | -0.00581582 | 0.0205778 | 0.0304233 | 0.0147074 | 0.0106175 | 0.0249612 | -0.00742326 | 0.0153477 | 0.000884112 | -0.000503387 | -0.0117098 | -0.053438 | 0.0182647 | -0.0279433 | -0.0149067 | -0.00594051 | 0.0176648 | 0.0160926 | 0.0132245 | 0.00580819 |
| mths_since_last_major_derog | -0.016988 | -0.0380772 | -0.0380772 | -0.0381205 | -0.0140784 | 0.0596923 | -0.0264476 | 0.0245972 | 0.0116384 | -0.0190358 | nan | -0.0273196 | 0.226492 | -0.112574 | -0.282168 | -0.282164 | 0.0821566 | 0.570468 | -0.000451319 | 0.0159066 | 0.0148058 | -0.0746145 | 0.00484744 | 0.12161 | -0.0163854 | 1 | -0.0138163 | -0.01033 | -0.0145247 | 0.0355687 | 0.0797902 | 0.035763 | 0.0460466 | 0.0300871 | 0.049313 | 0.0439697 | 0.0624107 | 0.0929728 | 0.0899842 | -0.115907 | 0.0576812 | 0.069348 | 0.00367465 | 0.0917672 | 0.0852758 | 0.0144104 | -0.125023 | 0.0105724 | 0.122622 | 0.0333099 | nan | nan | nan | nan | 0.066741 | -0.00798391 | 0.383217 | 0.0621868 | 0.40456 | 0.584653 | -0.0278289 | 0.0208443 | -0.038223 | 0.0409552 | 0.0925125 | -0.00827835 | 0.0716812 | 0.00823319 | 0.0135839 | 0.0468141 | -0.00173777 | 0.271122 | 0.097447 | -0.559342 | -0.00136059 | -0.0395159 | 0.019823 | -0.020143 | -0.00844678 | -0.0127033 | 0.0311284 |
| application_type | 0.146709 | 0.10254 | 0.10254 | 0.102532 | 0.0642611 | 0.0411864 | 0.0913125 | -0.0747383 | -0.0536792 | 0.131558 | nan | 0.189708 | -0.00262258 | 0.0119714 | 0.0178947 | 0.0178956 | -0.0223655 | -0.00666193 | -0.000572396 | -0.0184166 | -0.00585126 | -0.00300515 | 0.0246415 | -0.0146581 | 0.0156404 | -0.0138163 | 1 | 0.900298 | 0.925836 | -0.0022275 | -0.0156001 | -0.00278045 | -0.013887 | -0.0119551 | -0.00150543 | 0.011471 | -0.0107439 | -0.0262963 | -0.0262304 | 0.000584367 | 0.016174 | 0.0142427 | 0.0281037 | 0.00287337 | -0.0214221 | 0.0427614 | -0.0195873 | 0.019681 | -0.00785714 | -0.0028434 | nan | nan | nan | nan | 0.0332519 | -0.0190638 | -0.00984521 | 0.00122771 | -0.00934307 | -0.00791016 | -0.0311423 | -0.0208105 | -0.0395794 | -0.0383825 | -0.00106958 | -0.0229003 | -0.0268364 | -0.0160854 | -0.0177416 | -0.00323222 | 0.000418642 | -0.00540271 | -0.0241895 | 0.00370765 | 0.0252186 | 0.00528841 | -0.00976738 | 0.572076 | 0.504644 | 0.674253 | 0.446625 |
| annual_inc_joint | 0.138757 | 0.128165 | 0.128165 | 0.12816 | 0.0643084 | 0.0263215 | 0.113794 | -0.0584512 | -0.0215331 | 0.123699 | nan | 0.167284 | 0.00268839 | -0.00187587 | 0.0257033 | 0.025704 | -0.0208437 | -0.000829696 | -0.00698251 | -0.000557158 | -0.00959029 | 0.0172646 | 0.0259453 | 0.00482136 | 0.0196043 | -0.01033 | 0.900298 | 1 | 0.803658 | -0.000826569 | -0.0113082 | 0.00611445 | -0.00859938 | -0.00526274 | 0.00420784 | 0.0306911 | -0.0107355 | -0.0234255 | -0.0238085 | 0.0234647 | 0.0138116 | 0.0154923 | 0.0326106 | 0.00616531 | -0.0147903 | 0.0676954 | -0.00159911 | 0.0183556 | -0.00702118 | -0.00256141 | nan | nan | nan | nan | 0.0503214 | -0.0127925 | -0.00496629 | 0.00317881 | -0.00351716 | -0.00422056 | -0.0160728 | -0.00925781 | -0.0229321 | -0.0208823 | 0.00943032 | -0.00966986 | -0.0121454 | -0.00449231 | 0.000173295 | -0.0029595 | 0.0019704 | -0.00378128 | -0.0178815 | 0.0012872 | 0.0224391 | -0.0023287 | -0.00788305 | 0.634224 | 0.525145 | 0.628761 | 0.409268 |
| dti_joint | 0.138557 | 0.0989231 | 0.0989231 | 0.0989095 | 0.0637183 | 0.0560887 | 0.0907054 | -0.0694189 | -0.0542628 | 0.12324 | nan | 0.231641 | -0.00373691 | 0.00987256 | 0.010836 | 0.0108358 | -0.0196721 | -0.0068887 | -0.00275022 | -0.000784209 | -0.00793729 | 0.00648542 | 0.0342257 | 0.00074875 | 0.0126395 | -0.0145247 | 0.925836 | 0.803658 | 1 | -0.0020982 | -0.0118766 | 0.0116995 | -0.00107468 | 0.00566236 | 0.00630324 | 0.0286964 | 0.00122126 | -0.0239755 | -0.0222214 | 0.00748965 | 0.0274741 | 0.0204248 | 0.036244 | 0.00762963 | -0.0104125 | 0.0302414 | -0.0224684 | 0.0295882 | -0.00673212 | -0.0026222 | nan | nan | nan | nan | 0.0289413 | -0.0170789 | -0.0111498 | 0.00414795 | -0.0104194 | -0.00874896 | -0.018663 | -0.00485167 | -0.029926 | -0.0306313 | 0.0145116 | -0.0107856 | -0.0173175 | 0.000179617 | -0.000113842 | -0.0031668 | 0.000314872 | -0.00559278 | -0.0170224 | 0.00900865 | 0.0338727 | 0.00289668 | -0.0104434 | 0.610817 | 0.473685 | 0.655014 | 0.406984 |
| acc_now_delinq | -0.0130889 | 0.0017271 | 0.0017271 | 0.00170044 | -0.00242646 | 0.0065543 | 0.00338448 | 0.0108487 | 0.0128571 | -0.00929717 | nan | -0.00105407 | 0.123617 | -0.0314438 | -0.0428563 | -0.0428545 | -0.00414494 | 0.0689108 | -0.0054401 | 0.0224183 | -0.00257833 | 0.00352412 | -0.0303393 | 0.0288889 | -0.000441343 | 0.0355687 | -0.0022275 | -0.000826569 | -0.0020982 | 1 | -0.00761881 | 0.007005 | -0.00485305 | -0.00670858 | 0.00667966 | 0.00867382 | -0.00438624 | -0.00527453 | -0.00443329 | -0.00264773 | -0.0250433 | -0.00524045 | 0.00434626 | -0.00538684 | -0.00690889 | 0.0139345 | 0.0184271 | -0.0292296 | 0.0425989 | 0.203697 | nan | nan | nan | nan | 0.0262094 | -0.00634398 | 0.0495909 | -0.000893305 | 0.062975 | 0.0182332 | -0.00295717 | 0.00554845 | 0.00178831 | 0.0176981 | 0.00768579 | 0.0167498 | 0.0284013 | 0.00383753 | 0.0115189 | 0.40493 | 0.795964 | 0.0583675 | -0.00717039 | -0.0489672 | -0.0262401 | -0.0115422 | 0.00792196 | -0.00329687 | -0.00300911 | -0.00518892 | -0.00104258 |
| open_acc_6m | -0.0192934 | -0.0153681 | -0.0153681 | -0.0154125 | 0.0139953 | 0.170999 | 0.00464014 | 0.0226559 | 0.0373415 | -0.0237732 | nan | 0.0302801 | -0.000239808 | -0.0118584 | -0.0616149 | -0.0616173 | 0.424702 | 0.0491603 | 0.0661928 | 0.281506 | 0.0568102 | -0.0193387 | -0.181813 | 0.258755 | -0.0436611 | 0.0797902 | -0.0156001 | -0.0113082 | -0.0118766 | -0.00761881 | 1 | 0.0800516 | 0.38216 | 0.29324 | 0.0465493 | 0.11244 | 0.161022 | 0.616661 | 0.474521 | -0.0835329 | -0.0462331 | 0.153737 | 0.0913306 | 0.308557 | 0.549247 | -0.0335654 | 0.099022 | -0.160855 | 0.0018867 | -0.00455355 | nan | nan | nan | nan | 0.0499448 | 0.0257039 | 0.0328249 | 0.151626 | 0.035032 | 0.0597546 | 0.134118 | 0.206671 | 0.1967 | 0.191733 | 0.136626 | 0.277622 | 0.245263 | 0.19597 | 0.280231 | -0.00618333 | -0.00669984 | 0.0095132 | 0.720908 | 0.00667259 | -0.151688 | 0.0558972 | 0.0152902 | -0.0126559 | -0.00728085 | -0.0204226 | -0.00799011 |
| open_act_il | -0.00926293 | 0.0304857 | 0.0304857 | 0.0304666 | 0.0343187 | 0.0333187 | 0.0273263 | -0.0626386 | 0.0633142 | -0.0104031 | nan | 0.173504 | 0.0640688 | 0.0583278 | -0.0134152 | -0.0134182 | 0.0306383 | 0.0527387 | -0.0315764 | 0.51498 | -0.028641 | -0.0012892 | 0.0458067 | 0.373834 | 0.00142055 | 0.035763 | -0.00278045 | 0.00611445 | 0.0116995 | 0.007005 | 0.0800516 | 1 | 0.270178 | 0.35514 | 0.147997 | 0.556975 | 0.426248 | -0.0165188 | -0.0177898 | 0.0102881 | 0.371163 | 0.0892446 | 0.0855759 | 0.0774622 | 0.164241 | -0.0494235 | -0.0312765 | 0.045203 | -0.00194676 | -0.00280131 | nan | nan | nan | nan | -0.0309252 | -0.00353267 | 0.0142301 | 0.0499328 | 0.0199633 | 0.0883981 | -0.0070089 | -0.0021399 | -0.0173969 | -0.0285025 | 0.630083 | -0.00685534 | -0.0186272 | 0.00133266 | 0.517462 | 0.000432986 | 0.00593036 | 0.0614521 | 0.12235 | -0.0109671 | 0.0425085 | -0.0286686 | -0.0122823 | 0.00421186 | -0.00247352 | -0.00348556 | -0.000702324 |
| open_il_12m | -0.0218833 | 0.00200697 | 0.00200697 | 0.00198669 | 0.0419519 | 0.19403 | 0.0181085 | 0.0474697 | 0.0797827 | -0.0312628 | nan | 0.131945 | -0.00885671 | -0.00247454 | -0.0216399 | -0.0216427 | 0.145311 | 0.0388924 | 0.0486684 | 0.167873 | 0.0374344 | -0.0352578 | -0.0838585 | 0.254223 | -0.0390542 | 0.0460466 | -0.013887 | -0.00859938 | -0.00107468 | -0.00485305 | 0.38216 | 0.270178 | 1 | 0.754878 | 0.125331 | 0.297526 | 0.381355 | 0.0636213 | 0.067018 | -0.0637622 | 0.189743 | 0.259924 | 0.208412 | 0.320585 | 0.440723 | 0.0238506 | 0.00233076 | -0.079635 | -0.0027676 | -0.00445873 | nan | nan | nan | nan | 0.0575597 | -0.0132939 | 0.0187393 | 0.12935 | 0.0155649 | 0.0379043 | -0.0396431 | -0.0164143 | -0.00605558 | 0.0258083 | 0.346195 | 0.0223643 | 0.0443078 | -0.0193469 | 0.168107 | -0.00431037 | -0.00401501 | -0.00208664 | 0.561705 | 0.0264624 | -0.0685178 | 0.0463047 | 0.00654293 | -0.00521119 | -0.00469154 | -0.0112569 | -0.00325605 |
| open_il_24m | 0.00589419 | 0.0315075 | 0.0315075 | 0.0314859 | 0.0627368 | 0.171914 | 0.0411935 | 0.0610861 | 0.102467 | -0.00431884 | nan | 0.180339 | -0.0258594 | 0.00875091 | -0.017437 | -0.0174411 | 0.117973 | 0.0373714 | 0.0540555 | 0.237871 | 0.0381032 | -0.0244266 | -0.0664247 | 0.348491 | -0.0274876 | 0.0300871 | -0.0119551 | -0.00526274 | 0.00566236 | -0.00670858 | 0.29324 | 0.35514 | 0.754878 | 1 | 0.165277 | 0.365932 | 0.396888 | 0.0518012 | 0.0782004 | -0.0536387 | 0.208603 | 0.343601 | 0.2814 | 0.264266 | 0.574646 | 0.0240117 | 0.00714786 | -0.0613072 | -0.00237151 | -0.00463704 | nan | nan | nan | nan | 0.0783013 | -0.0170853 | 0.0081386 | 0.151223 | 0.00441684 | 0.0280061 | -0.0254716 | 0.000797204 | 0.000984645 | 0.0392207 | 0.470968 | 0.0505349 | 0.0642075 | 0.00639011 | 0.239377 | -0.00534064 | -0.00540845 | -0.022563 | 0.431285 | 0.0651893 | -0.0520546 | 0.0526413 | 0.00415751 | -0.00377276 | -0.00241752 | -0.0122527 | -0.00416827 |
| mths_since_rcnt_il | -0.00312323 | 0.0499328 | 0.0499328 | 0.0499276 | 0.0352736 | -0.0102362 | 0.0420945 | 0.0523735 | 0.0499242 | -0.00258569 | nan | 0.11519 | 0.0277418 | -0.036872 | -0.00255944 | -0.00256083 | 0.0198792 | 0.0681927 | 0.0218196 | 0.108818 | 0.0166762 | 0.0215199 | 0.01289 | 0.16105 | 0.0124691 | 0.049313 | -0.00150543 | 0.00420784 | 0.00630324 | 0.00667966 | 0.0465493 | 0.147997 | 0.125331 | 0.165277 | 1 | 0.134028 | 0.307842 | -6.7887e-05 | -0.000691562 | 0.0229712 | 0.100231 | 0.0744073 | 0.0835565 | 0.0620311 | 0.0881004 | 0.0497583 | -0.00460383 | 0.01612 | 0.00873132 | 0.0020377 | nan | nan | nan | nan | 0.0741814 | -0.00596338 | 0.0249249 | 0.0588964 | 0.0323102 | 0.0352813 | -0.00245439 | 0.0172447 | 0.00293162 | 0.0289562 | 0.184509 | 0.0271208 | 0.0518723 | 0.0171077 | 0.108734 | 0.00112023 | 0.00523758 | 0.0163838 | 0.0683615 | -0.0239216 | 0.0133864 | 0.0212014 | 0.00247218 | -0.000141324 | 0.00206934 | -0.00470371 | -0.00408784 |
| total_bal_il | 0.0045272 | 0.157087 | 0.157087 | 0.157077 | 0.0769668 | 0.0376085 | 0.146332 | -0.00350992 | 0.201533 | 0.0028623 | nan | 0.192938 | 0.0485201 | -0.0285505 | 0.0117294 | 0.0117265 | 0.0504319 | 0.0643094 | -0.0264372 | 0.342034 | -0.0223009 | 0.0925043 | 0.0328792 | 0.407291 | 0.0131395 | 0.0439697 | 0.011471 | 0.0306911 | 0.0286964 | 0.00867382 | 0.11244 | 0.556975 | 0.297526 | 0.365932 | 0.134028 | 1 | 0.345969 | -0.000897383 | -0.00296172 | 0.0971311 | 0.294235 | 0.145066 | 0.11999 | 0.149457 | 0.190607 | 0.191673 | 0.0400949 | 0.0233544 | 0.00109849 | -0.000601912 | nan | nan | nan | nan | 0.0811057 | 0.00422169 | 0.0201831 | 0.0801214 | 0.024244 | 0.0713369 | 0.0257781 | 0.0279362 | 0.0347752 | 0.042446 | 0.579082 | 0.0427055 | 0.0503017 | 0.0300477 | 0.34343 | 0.00390574 | 0.00531998 | 0.0370412 | 0.156345 | 0.00849149 | 0.0168712 | -0.0283581 | 0.000789849 | 0.0222411 | 0.0140907 | 0.00881707 | 0.00681597 |
| il_util | -0.0244249 | -0.0383439 | -0.0383439 | -0.0383544 | 0.042335 | 0.125128 | -0.0330059 | -0.019046 | 0.00228085 | -0.0295579 | nan | 0.165749 | 0.0141296 | 0.0601059 | -0.0818219 | -0.0818255 | 0.0717362 | 0.0429723 | 0.0319805 | 0.21282 | 0.0232084 | -0.0592588 | -0.00230849 | 0.185937 | -0.0197974 | 0.0624107 | -0.0107439 | -0.0107355 | 0.00122126 | -0.00438624 | 0.161022 | 0.426248 | 0.381355 | 0.396888 | 0.307842 | 0.345969 | 1 | 0.0298274 | 0.0387439 | -0.0700049 | 0.511279 | 0.151635 | 0.0935839 | 0.149637 | 0.231345 | -0.036966 | -0.0735395 | 0.00819367 | 0.00013921 | -0.00579825 | nan | nan | nan | nan | -0.0222128 | -0.00788238 | 0.0086735 | 0.113513 | 0.0106431 | 0.0716097 | -0.0312738 | -0.00813602 | -0.0307332 | -0.0396905 | 0.328646 | -0.0140658 | -0.0209398 | -0.0139502 | 0.2131 | -0.00287632 | -0.00583002 | 0.0332349 | 0.216616 | -0.0211727 | 0.00629931 | 0.0326699 | -0.00220551 | -0.0121975 | -0.0122938 | -0.0133814 | -0.00771851 |
| open_rv_12m | -0.0139552 | -0.034697 | -0.034697 | -0.0347373 | -0.0146462 | 0.150425 | -0.00946319 | 0.00905316 | -0.00400232 | -0.0136302 | nan | -0.000557731 | -0.0234147 | 0.00055375 | -0.109483 | -0.109485 | 0.341016 | 0.0417107 | 0.093185 | 0.369619 | 0.0807145 | -0.00142028 | -0.197121 | 0.257423 | -0.0463635 | 0.0929728 | -0.0262963 | -0.0234255 | -0.0239755 | -0.00527453 | 0.616661 | -0.0165188 | 0.0636213 | 0.0518012 | -6.7887e-05 | -0.000897383 | 0.0298274 | 1 | 0.773266 | -0.0893363 | -0.161308 | 0.0942083 | 0.00654725 | 0.311212 | 0.648032 | -0.149873 | 0.130773 | -0.15384 | -0.000782398 | -0.00272689 | nan | nan | nan | nan | -0.0264225 | 0.0543993 | 0.0314165 | 0.158242 | 0.0336248 | 0.070966 | 0.273071 | 0.369663 | 0.332636 | 0.300954 | 0.0124119 | 0.455826 | 0.376212 | 0.356516 | 0.367311 | -0.00472218 | -0.00491244 | -0.000297686 | 0.837077 | 0.00695222 | -0.155791 | 0.0796004 | 0.0217304 | -0.0186303 | -0.0179965 | -0.0269882 | -0.0106544 |
| open_rv_24m | -0.0177426 | -0.017261 | -0.017261 | -0.0173085 | -0.00613441 | 0.158809 | 0.00898484 | 0.00405258 | -0.0103785 | -0.0139293 | nan | 0.0239507 | -0.0489541 | 0.024354 | -0.133455 | -0.133456 | 0.298656 | 0.0353053 | 0.122713 | 0.473997 | 0.103701 | 0.00277935 | -0.215097 | 0.321715 | -0.044386 | 0.0899842 | -0.0262304 | -0.0238085 | -0.0222214 | -0.00443329 | 0.474521 | -0.0177898 | 0.067018 | 0.0782004 | -0.000691562 | -0.00296172 | 0.0387439 | 0.773266 | 1 | -0.103536 | -0.175801 | 0.13306 | 0.0131964 | 0.297171 | 0.842025 | -0.196518 | 0.147483 | -0.152172 | 0.00408837 | -0.00160363 | nan | nan | nan | nan | -0.0467358 | 0.0640103 | 0.02146 | 0.199499 | 0.0255257 | 0.0694876 | 0.346665 | 0.461232 | 0.412793 | 0.377464 | 0.0117532 | 0.584674 | 0.476877 | 0.45121 | 0.471632 | -0.00401581 | -0.00452248 | -0.0229226 | 0.654754 | 0.0397782 | -0.153979 | 0.111202 | 0.0252402 | -0.020129 | -0.0217354 | -0.0307799 | -0.0121039 |
| max_bal_bc | -0.0219051 | 0.353818 | 0.353818 | 0.353833 | 0.0903663 | -0.0424092 | 0.331862 | 0.0713191 | 0.24945 | -0.0016616 | nan | 0.0851506 | -0.0515156 | -0.190607 | 0.0460722 | 0.0460666 | -0.0542851 | -0.0817066 | -0.144157 | 0.106785 | -0.114796 | 0.546885 | 0.3258 | 0.121812 | 0.0388421 | -0.115907 | 0.000584367 | 0.0234647 | 0.00748965 | -0.00264773 | -0.0835329 | 0.0102881 | -0.0637622 | -0.0536387 | 0.0229712 | 0.0971311 | -0.0700049 | -0.0893363 | -0.103536 | 1 | 0.131186 | -0.0786183 | -0.0194046 | -0.078415 | -0.0993511 | 0.255621 | 0.177813 | 0.305404 | -0.016073 | -0.00398507 | nan | nan | nan | nan | 0.212922 | 0.108332 | -0.0603317 | -0.0491943 | -0.062233 | -0.0997827 | 0.217399 | 0.124489 | 0.206914 | 0.182499 | 0.0141902 | 0.0895558 | 0.116729 | 0.11929 | 0.106525 | -0.00680483 | 0.00441199 | -0.0462697 | -0.0968394 | 0.128772 | 0.217872 | -0.129389 | -0.0217796 | 0.0469201 | 0.0172944 | 0.00881723 | -0.0134969 |
| all_util | -0.0811412 | 0.0146875 | 0.0146875 | 0.0146675 | 0.050348 | 0.25345 | 0.0367535 | -0.0147168 | 0.0067374 | -0.0746282 | nan | 0.170675 | 0.0213504 | 0.0536393 | -0.416169 | -0.41617 | -0.048355 | 0.053505 | -0.0136064 | -0.0123935 | -0.00883367 | 0.109251 | 0.65511 | -0.00023709 | -0.0430698 | 0.0576812 | 0.016174 | 0.0138116 | 0.0274741 | -0.0250433 | -0.0462331 | 0.371163 | 0.189743 | 0.208603 | 0.100231 | 0.294235 | 0.511279 | -0.161308 | -0.175801 | 0.131186 | 1 | 0.0754531 | 0.062601 | 0.0274413 | -0.0408526 | 0.0913201 | -0.47187 | 0.565641 | -0.00831581 | -0.00685925 | nan | nan | nan | nan | -0.0244293 | -0.0292938 | 0.018079 | 0.0191668 | 0.0149528 | 0.0760154 | -0.0328463 | -0.0213858 | -0.2125 | -0.220668 | 0.256207 | -0.249764 | -0.228374 | -0.0026557 | -0.0110648 | -0.00707463 | -0.0217334 | 0.0324189 | -0.0402328 | -0.0762987 | 0.478858 | -0.0158144 | -0.00264618 | 0.00676926 | -0.00215361 | 0.016229 | -0.00191463 |
| inq_fi | 0.0338149 | 0.00764786 | 0.00764786 | 0.00763789 | 0.027585 | 0.145232 | 0.0209666 | 0.0092254 | 0.053162 | 0.0260245 | nan | 0.055609 | 0.0229035 | 0.0292811 | -0.0815677 | -0.0815686 | 0.218088 | 0.073993 | 0.0762482 | 0.108395 | 0.0674735 | -0.0468321 | -0.0734558 | 0.16573 | -0.0301753 | 0.069348 | 0.0142427 | 0.0154923 | 0.0204248 | -0.00524045 | 0.153737 | 0.0892446 | 0.259924 | 0.343601 | 0.0744073 | 0.145066 | 0.151635 | 0.0942083 | 0.13306 | -0.0786183 | 0.0754531 | 1 | 0.0898335 | 0.563693 | 0.302884 | 0.0517894 | -0.00437465 | -0.0618109 | 0.0107957 | -0.000685353 | nan | nan | nan | nan | 0.090355 | 0.00264659 | 0.041065 | 0.211763 | 0.0369582 | 0.0677987 | -0.000722646 | 0.00851313 | 0.0132135 | 0.049748 | 0.173357 | 0.0622347 | 0.0629156 | 0.021789 | 0.109912 | -0.00292626 | -0.0065604 | 0.00873659 | 0.228857 | -0.017259 | -0.0614376 | 0.0731705 | 0.0202849 | 0.00407757 | 0.0118063 | 0.00628767 | 0.0133634 |
| total_cu_tl | 0.00451937 | 0.0695528 | 0.0695528 | 0.0695417 | 0.0538909 | 0.0204437 | 0.0597467 | 0.0834455 | 0.0419613 | 0.00432069 | nan | 0.09578 | 0.0172532 | -0.0403152 | -0.00267085 | -0.00267086 | 0.0313672 | 0.0386346 | 0.00847277 | 0.101001 | -0.00157323 | 0.0357439 | 0.0355184 | 0.29089 | 0.0112963 | 0.00367465 | 0.0281037 | 0.0326106 | 0.036244 | 0.00434626 | 0.0913306 | 0.0855759 | 0.208412 | 0.2814 | 0.0835565 | 0.11999 | 0.0935839 | 0.00654725 | 0.0131964 | -0.0194046 | 0.062601 | 0.0898335 | 1 | 0.0789875 | 0.165896 | 0.0591046 | -0.0162122 | -0.0169612 | 0.00400361 | 0.00055474 | nan | nan | nan | nan | 0.165511 | -0.0855086 | 0.000451111 | 0.0373086 | 0.025179 | -0.00219945 | -0.0817919 | 0.0158443 | -0.0685984 | 0.00226729 | 0.304966 | 0.0507981 | 0.112094 | 0.0266929 | 0.101881 | 0.00140141 | 0.00234959 | -0.00263983 | 0.121665 | 0.0470462 | -0.000464966 | 0.0172131 | -0.014358 | 0.0245087 | 0.0345538 | 0.0207548 | 0.00824441 |
| inq_last_12m | -0.00235927 | 0.0164975 | 0.0164975 | 0.0164552 | 0.0303021 | 0.1967 | 0.037195 | 0.000861288 | 0.064537 | -0.00388903 | nan | 0.0308119 | 0.0264162 | 0.0223407 | -0.126787 | -0.126788 | 0.484791 | 0.074587 | 0.105516 | 0.184489 | 0.0904722 | -0.0253142 | -0.123023 | 0.213623 | -0.0453963 | 0.0917672 | 0.00287337 | 0.00616531 | 0.00762963 | -0.00538684 | 0.308557 | 0.0774622 | 0.320585 | 0.264266 | 0.0620311 | 0.149457 | 0.149637 | 0.311212 | 0.297171 | -0.078415 | 0.0274413 | 0.563693 | 0.0789875 | 1 | 0.398087 | 0.0410276 | 0.0248997 | -0.101845 | 0.00925652 | -0.003314 | nan | nan | nan | nan | 0.0974853 | 0.0163791 | 0.0450641 | 0.283332 | 0.0430992 | 0.0777948 | 0.0551404 | 0.0958435 | 0.0992322 | 0.120394 | 0.148112 | 0.159422 | 0.155754 | 0.0933912 | 0.183727 | -0.00495738 | -0.00562695 | 0.0160847 | 0.448994 | -0.0192305 | -0.103555 | 0.104239 | 0.0228439 | -0.00750618 | 0.00157327 | -0.00676538 | 0.00452644 |
| acc_open_past_24mths | -0.00409831 | 0.019626 | 0.019626 | 0.0195828 | 0.0368651 | 0.208535 | 0.0424672 | 0.038457 | 0.0610748 | -0.00624408 | nan | 0.101628 | -0.0585419 | 0.021144 | -0.10921 | -0.109214 | 0.303038 | 0.047586 | 0.123906 | 0.51123 | 0.0998176 | -0.00365459 | -0.209715 | 0.455349 | -0.0452869 | 0.0852758 | -0.0214221 | -0.0147903 | -0.0104125 | -0.00690889 | 0.549247 | 0.164241 | 0.440723 | 0.574646 | 0.0881004 | 0.190607 | 0.231345 | 0.648032 | 0.842025 | -0.0993511 | -0.0408526 | 0.302884 | 0.165896 | 0.398087 | 1 | -0.0803721 | 0.133892 | -0.15783 | 0.00283212 | -0.00440563 | nan | nan | nan | nan | 0.0719896 | 0.0427255 | 0.0199098 | 0.256422 | 0.020815 | 0.0679818 | 0.264383 | 0.368763 | 0.335204 | 0.328338 | 0.2571 | 0.496488 | 0.42004 | 0.363311 | 0.510226 | -0.00651912 | -0.00613127 | -0.0341718 | 0.771942 | 0.0729495 | -0.154705 | 0.114637 | 0.0208618 | -0.0135357 | -0.00818275 | -0.0270999 | -0.0106693 |
| avg_cur_bal | 0.027256 | 0.215998 | 0.215998 | 0.216055 | 0.0757197 | -0.0883193 | 0.184681 | 0.0955353 | 0.287459 | 0.0272625 | nan | -0.0724721 | 0.0317852 | -0.118321 | 0.10444 | 0.104442 | -0.0440917 | 0.0490007 | -0.0736874 | -0.124814 | -0.0579094 | 0.281683 | 0.142375 | 0.0360446 | 0.0415878 | 0.0144104 | 0.0427614 | 0.0676954 | 0.0302414 | 0.0139345 | -0.0335654 | -0.0494235 | 0.0238506 | 0.0240117 | 0.0497583 | 0.191673 | -0.036966 | -0.149873 | -0.196518 | 0.255621 | 0.0913201 | 0.0517894 | 0.0591046 | 0.0410276 | -0.0803721 | 1 | 0.028102 | 0.0720545 | 0.00425261 | 0.0225698 | nan | nan | nan | nan | 0.462622 | -0.0506585 | 0.0296503 | 0.0319576 | 0.0231014 | 0.013306 | -0.113039 | -0.149751 | -0.118107 | -0.0580143 | 0.0448754 | -0.200781 | -0.0945567 | -0.153349 | -0.125071 | 0.00738516 | 0.013113 | 0.00446792 | -0.0495513 | -0.0435262 | 0.0709961 | -0.0807372 | -0.00148414 | 0.0469711 | 0.0618051 | 0.0416053 | 0.0160454 |
| bc_open_to_buy | 0.0610435 | 0.188939 | 0.188939 | 0.188967 | 0.0209426 | -0.244328 | 0.150411 | 0.0212737 | 0.153362 | 0.0629564 | nan | -0.063867 | -0.061073 | -0.140977 | 0.507928 | 0.507929 | 0.0318196 | -0.111267 | -0.113859 | 0.305943 | -0.0907413 | 0.150496 | -0.464889 | 0.244732 | 0.0553371 | -0.125023 | -0.0195873 | -0.00159911 | -0.0224684 | 0.0184271 | 0.099022 | -0.0312765 | 0.00233076 | 0.00714786 | -0.00460383 | 0.0400949 | -0.0735395 | 0.130773 | 0.147483 | 0.177813 | -0.47187 | -0.00437465 | -0.0162122 | 0.0248997 | 0.133892 | 0.028102 | 1 | -0.500072 | -0.0114597 | 0.00159527 | nan | nan | nan | nan | 0.133413 | 0.0748705 | -0.063607 | 0.00754838 | -0.0748285 | -0.0818461 | 0.245744 | 0.105946 | 0.484976 | 0.459524 | -0.00451172 | 0.371504 | 0.331844 | 0.122936 | 0.309204 | 0.00566647 | 0.0168384 | -0.0442112 | 0.117169 | 0.165385 | -0.411689 | -0.0997203 | -0.0197553 | 0.0149981 | 0.00844124 | -0.0193241 | -0.00872533 |
| bc_util | -0.091366 | 0.0761079 | 0.0761079 | 0.0760853 | 0.0446585 | 0.210012 | 0.098197 | 0.0420823 | 0.0121136 | -0.0728878 | nan | 0.150757 | -0.00470567 | -0.0234079 | -0.470744 | -0.470741 | -0.0897593 | 0.0182409 | -0.0168851 | -0.0741913 | -0.0153993 | 0.191461 | 0.841099 | -0.0744188 | -0.028589 | 0.0105724 | 0.019681 | 0.0183556 | 0.0295882 | -0.0292296 | -0.160855 | 0.045203 | -0.079635 | -0.0613072 | 0.01612 | 0.0233544 | 0.00819367 | -0.15384 | -0.152172 | 0.305404 | 0.565641 | -0.0618109 | -0.0169612 | -0.101845 | -0.15783 | 0.0720545 | -0.500072 | 1 | -0.00983678 | -0.00690652 | nan | nan | nan | nan | 0.0105076 | 0.197854 | -0.000993264 | -0.0406667 | 0.00334719 | -0.0165676 | 0.117683 | 0.154295 | -0.128802 | -0.153372 | 0.0135313 | -0.124005 | -0.123005 | 0.161241 | -0.0750139 | -0.012314 | -0.0213142 | -0.0120727 | -0.171062 | -0.0324937 | 0.845272 | -0.018847 | -0.00370829 | 0.0106302 | -0.00364648 | 0.0180859 | -0.00833916 |
| chargeoff_within_12_mths | -0.000865381 | -0.00195514 | -0.00195514 | -0.00196807 | -0.00225203 | 0.00647974 | -0.000521834 | 0.00673721 | 0.00481134 | 0.00153744 | nan | -0.00259618 | 0.152355 | -0.0266618 | -0.0528395 | -0.052837 | 0.0102862 | 0.0702054 | -0.011433 | 0.005978 | -0.00895587 | -0.00967216 | -0.0120491 | 0.0361676 | -0.000363524 | 0.122622 | -0.00785714 | -0.00702118 | -0.00673212 | 0.0425989 | 0.0018867 | -0.00194676 | -0.0027676 | -0.00237151 | 0.00873132 | 0.00109849 | 0.00013921 | -0.000782398 | 0.00408837 | -0.016073 | -0.00831581 | 0.0107957 | 0.00400361 | 0.00925652 | 0.00283212 | 0.00425261 | -0.0114597 | -0.00983678 | 1 | 0.0105278 | nan | nan | nan | nan | 0.019213 | 0.000453322 | 0.0558066 | 0.00759287 | 0.0591205 | 0.114215 | -0.00658847 | 0.0038822 | -0.00124331 | 0.0293433 | 0.00961118 | 0.00677495 | 0.038421 | 0.00106656 | 0.00553998 | 0.0359113 | 0.000856444 | 0.227076 | -0.00218708 | -0.0806482 | -0.0101618 | -0.0154123 | -0.000991514 | -0.0034651 | -0.00369874 | -0.00350607 | 0.0031394 |
| delinq_amnt | -0.00363466 | 0.00276253 | 0.00276253 | 0.00274976 | 0.000162738 | 0.00431558 | 0.00357904 | 0.00203488 | 0.00818791 | -0.00383244 | nan | -0.00724449 | 0.035054 | -0.00867312 | -0.0151574 | -0.0151572 | -0.00224899 | 0.0207741 | 0.000426055 | 0.00331224 | 0.00281559 | 0.00223265 | -0.00763357 | 0.0043422 | -0.00318297 | 0.0333099 | -0.0028434 | -0.00256141 | -0.0026222 | 0.203697 | -0.00455355 | -0.00280131 | -0.00445873 | -0.00463704 | 0.0020377 | -0.000601912 | -0.00579825 | -0.00272689 | -0.00160363 | -0.00398507 | -0.00685925 | -0.000685353 | 0.00055474 | -0.003314 | -0.00440563 | 0.0225698 | 0.00159527 | -0.00690652 | 0.0105278 | 1 | nan | nan | nan | nan | 0.0150131 | -0.00164215 | 0.00571492 | -0.0035332 | 0.00885733 | 0.0220937 | -0.001736 | 0.00036987 | -0.000381842 | 0.00136466 | -0.00188584 | 0.00155509 | 0.00386748 | -0.000938581 | -0.00123076 | 0.367772 | 0.0381949 | 0.0505135 | -0.00524767 | -0.0171228 | -0.00763145 | -0.0029183 | 0.0054966 | -0.00196265 | -0.00178863 | -0.00237695 | -0.00176629 |
| mo_sin_old_il_acct | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| mo_sin_old_rev_tl_op | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| mo_sin_rcnt_rev_tl_op | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| mo_sin_rcnt_tl | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| mort_acc | -0.0277226 | 0.212504 | 0.212504 | 0.212543 | 0.0901313 | -0.111063 | 0.175254 | 0.158505 | 0.198136 | -0.0154189 | nan | -0.0128473 | 0.0571907 | -0.27796 | 0.104666 | 0.104667 | 0.0126668 | 0.107099 | -0.0264072 | 0.128376 | -0.0255759 | 0.216294 | 0.0337002 | 0.347698 | 0.0511063 | 0.066741 | 0.0332519 | 0.0503214 | 0.0289413 | 0.0262094 | 0.0499448 | -0.0309252 | 0.0575597 | 0.0783013 | 0.0741814 | 0.0811057 | -0.0222128 | -0.0264225 | -0.0467358 | 0.212922 | -0.0244293 | 0.090355 | 0.165511 | 0.0974853 | 0.0719896 | 0.462622 | 0.133413 | 0.0105076 | 0.019213 | 0.0150131 | nan | nan | nan | nan | 1 | 0.000482828 | 0.0538963 | 0.0706693 | 0.0616658 | 0.0442576 | 0.0428493 | 0.0609731 | 0.0794778 | 0.180937 | 0.0846755 | 0.0840111 | 0.211166 | 0.0582078 | 0.126177 | 0.0097598 | 0.0217753 | 0.0132967 | 0.0666973 | -0.0144858 | 0.0126638 | -0.0174074 | -0.0178562 | 0.0361079 | 0.0868426 | 0.0186783 | 0.00742479 |
| mths_since_recent_bc | -0.0151451 | 0.0462264 | 0.0462264 | 0.0462363 | 0.0152259 | -0.0289126 | 0.0421676 | 0.00463817 | 0.0176033 | -0.00796961 | nan | 0.00830909 | -0.0158934 | -0.00855518 | 0.00784537 | 0.00784578 | 0.0173994 | -0.0147326 | 0.00549048 | 0.0907996 | 0.005651 | 0.0414679 | 0.0104042 | 0.0360243 | 0.0128764 | -0.00798391 | -0.0190638 | -0.0127925 | -0.0170789 | -0.00634398 | 0.0257039 | -0.00353267 | -0.0132939 | -0.0170853 | -0.00596338 | 0.00422169 | -0.00788238 | 0.0543993 | 0.0640103 | 0.108332 | -0.0292938 | 0.00264659 | -0.0855086 | 0.0163791 | 0.0427255 | -0.0506585 | 0.0748705 | 0.197854 | 0.000453322 | -0.00164215 | nan | nan | nan | nan | 0.000482828 | 1 | -0.0010856 | 0.016384 | -0.0138369 | -0.0130127 | 0.165875 | 0.103923 | 0.163902 | 0.116076 | -0.0184111 | 0.110806 | 0.069867 | 0.105189 | 0.0908174 | -0.0011123 | -0.0060109 | -0.0106523 | 0.0373853 | 0.0315371 | 0.116542 | 0.00425672 | 0.00565532 | -0.00607725 | -0.0114841 | -0.0225751 | -0.014197 |
| mths_since_recent_bc_dlq | -0.0203849 | -0.0068254 | -0.0068254 | -0.00685981 | -0.00936181 | 0.0239265 | -0.000387367 | 0.0261163 | 0.0295602 | -0.0223056 | nan | -0.0249932 | 0.317727 | -0.120467 | -0.202268 | -0.202266 | 0.0349001 | 0.550657 | -0.05204 | 0.0272822 | -0.0322887 | -0.0326004 | -0.00121411 | 0.126063 | -0.00611035 | 0.383217 | -0.00984521 | -0.00496629 | -0.0111498 | 0.0495909 | 0.0328249 | 0.0142301 | 0.0187393 | 0.0081386 | 0.0249249 | 0.0201831 | 0.0086735 | 0.0314165 | 0.02146 | -0.0603317 | 0.018079 | 0.041065 | 0.000451111 | 0.0450641 | 0.0199098 | 0.0296503 | -0.063607 | -0.000993264 | 0.0558066 | 0.00571492 | nan | nan | nan | nan | 0.0538963 | -0.0010856 | 1 | 0.0289446 | 0.777202 | 0.319289 | -0.01046 | 0.00367685 | -0.0108362 | 0.14231 | 0.038387 | 0.0165989 | 0.136835 | 0.00477741 | 0.0263332 | 0.0124995 | 0.045094 | 0.114447 | 0.0341549 | -0.520245 | -0.00849793 | -0.0823344 | 0.0113915 | -0.0135996 | -0.00428139 | -0.00994155 | 0.016833 |
| mths_since_recent_inq | 0.00170865 | 0.0138877 | 0.0138877 | 0.0138697 | 0.0248917 | 0.095931 | 0.0207774 | 0.00435118 | 0.0316428 | 0.000616289 | nan | 0.0291981 | 0.0128847 | 0.019383 | -0.094217 | -0.0942215 | 0.209879 | 0.0537591 | 0.0695888 | 0.128645 | 0.0573158 | -0.00693511 | -0.063428 | 0.132395 | -0.0138002 | 0.0621868 | 0.00122771 | 0.00317881 | 0.00414795 | -0.000893305 | 0.151626 | 0.0499328 | 0.12935 | 0.151223 | 0.0588964 | 0.0801214 | 0.113513 | 0.158242 | 0.199499 | -0.0491943 | 0.0191668 | 0.211763 | 0.0373086 | 0.283332 | 0.256422 | 0.0319576 | 0.00754838 | -0.0406667 | 0.00759287 | -0.0035332 | nan | nan | nan | nan | 0.0706693 | 0.016384 | 0.0289446 | 1 | 0.0319992 | 0.0444397 | 0.0569204 | 0.090525 | 0.0780788 | 0.079089 | 0.0785013 | 0.112131 | 0.107386 | 0.0857591 | 0.127483 | -0.00225567 | -0.00016021 | 0.00881176 | 0.211114 | -0.017555 | -0.0454136 | 0.0656321 | 0.0119967 | -0.00719881 | 0.00214206 | -0.00855814 | -0.000433285 |
| mths_since_recent_revol_delinq | -0.0187132 | -0.00389924 | -0.00389924 | -0.00393152 | -0.0117617 | 0.0266176 | 0.00415115 | 0.030035 | 0.0280972 | -0.0197745 | nan | -0.0196061 | 0.352637 | -0.132552 | -0.236348 | -0.236346 | 0.0338875 | 0.707576 | -0.0663142 | 0.0595541 | -0.0431191 | -0.0274949 | -0.00476487 | 0.153044 | -0.00568527 | 0.40456 | -0.00934307 | -0.00351716 | -0.0104194 | 0.062975 | 0.035032 | 0.0199633 | 0.0155649 | 0.00441684 | 0.0323102 | 0.024244 | 0.0106431 | 0.0336248 | 0.0255257 | -0.062233 | 0.0149528 | 0.0369582 | 0.025179 | 0.0430992 | 0.020815 | 0.0231014 | -0.0748285 | 0.00334719 | 0.0591205 | 0.00885733 | nan | nan | nan | nan | 0.0616658 | -0.0138369 | 0.777202 | 0.0319992 | 1 | 0.302119 | -0.0252805 | 0.0250356 | -0.0238033 | 0.108153 | 0.0488154 | 0.050745 | 0.165765 | 0.0252108 | 0.0581276 | 0.0164159 | 0.0572876 | 0.116126 | 0.0339703 | -0.550831 | -0.00396584 | -0.0982129 | 0.00937857 | -0.0130986 | -0.0033705 | -0.00786351 | 0.0195779 |
| num_accts_ever_120_pd | -0.00189675 | -0.043254 | -0.043254 | -0.0432889 | -0.0171136 | 0.0362125 | -0.0346932 | 0.00493574 | 0.00959516 | -0.00689764 | nan | -0.0274789 | 0.216559 | -0.0854754 | -0.194331 | -0.194328 | 0.0462168 | 0.362446 | -0.0108138 | 0.0314553 | 0.00321878 | -0.070373 | -0.0197588 | 0.145121 | -0.0122598 | 0.584653 | -0.00791016 | -0.00422056 | -0.00874896 | 0.0182332 | 0.0597546 | 0.0883981 | 0.0379043 | 0.0280061 | 0.0352813 | 0.0713369 | 0.0716097 | 0.070966 | 0.0694876 | -0.0997827 | 0.0760154 | 0.0677987 | -0.00219945 | 0.0777948 | 0.0679818 | 0.013306 | -0.0818461 | -0.0165676 | 0.114215 | 0.0220937 | nan | nan | nan | nan | 0.0442576 | -0.0130127 | 0.319289 | 0.0444397 | 0.302119 | 1 | -0.0440399 | -0.0241404 | -0.0627028 | 0.0522256 | 0.145643 | -0.0189137 | 0.0642757 | -0.0157037 | 0.0322784 | 0.0322363 | -0.00596655 | 0.317129 | 0.0752611 | -0.589166 | -0.0238768 | -0.0468898 | 0.00973285 | -0.0138636 | -0.00630334 | -0.00817286 | 0.0199888 |
| num_actv_bc_tl | -0.0346523 | 0.199607 | 0.199607 | 0.199572 | 0.0527023 | 0.0534045 | 0.20317 | 0.0758387 | 0.106956 | -0.0152928 | nan | 0.126229 | -0.0328287 | -0.116417 | -0.120806 | -0.120807 | 0.101559 | -0.0361648 | -0.0466023 | 0.551326 | -0.029659 | 0.309452 | 0.114271 | 0.304611 | 0.00199673 | -0.0278289 | -0.0311423 | -0.0160728 | -0.018663 | -0.00295717 | 0.134118 | -0.0070089 | -0.0396431 | -0.0254716 | -0.00245439 | 0.0257781 | -0.0312738 | 0.273071 | 0.346665 | 0.217399 | -0.0328463 | -0.000722646 | -0.0817919 | 0.0551404 | 0.264383 | -0.113039 | 0.245744 | 0.117683 | -0.00658847 | -0.001736 | nan | nan | nan | nan | 0.0428493 | 0.165875 | -0.01046 | 0.0569204 | -0.0252805 | -0.0440399 | 1 | 0.820425 | 0.82043 | 0.595364 | -0.0194805 | 0.650644 | 0.45893 | 0.808971 | 0.548949 | -0.00628703 | 0.000230749 | -0.029503 | 0.197673 | 0.109193 | 0.0581039 | -0.056417 | 0.0133264 | 0.00282292 | -0.016675 | -0.0271403 | -0.0203711 |
| num_actv_rev_tl | -0.0549845 | 0.161251 | 0.161251 | 0.161207 | 0.0559652 | 0.0947076 | 0.168627 | 0.111782 | 0.0792329 | -0.032089 | nan | 0.181384 | 0.00268074 | -0.15247 | -0.19519 | -0.19519 | 0.156783 | 0.0130495 | 0.00624367 | 0.661589 | 0.00564454 | 0.309683 | 0.122157 | 0.404457 | -0.00581582 | 0.0208443 | -0.0208105 | -0.00925781 | -0.00485167 | 0.00554845 | 0.206671 | -0.0021399 | -0.0164143 | 0.000797204 | 0.0172447 | 0.0279362 | -0.00813602 | 0.369663 | 0.461232 | 0.124489 | -0.0213858 | 0.00851313 | 0.0158443 | 0.0958435 | 0.368763 | -0.149751 | 0.105946 | 0.154295 | 0.0038822 | 0.00036987 | nan | nan | nan | nan | 0.0609731 | 0.103923 | 0.00367685 | 0.090525 | 0.0250356 | -0.0241404 | 0.820425 | 1 | 0.666762 | 0.489308 | 0.014075 | 0.776673 | 0.574335 | 0.975518 | 0.656357 | -0.00478624 | 0.00764551 | -0.0145671 | 0.286416 | 0.084511 | 0.111197 | -0.00444145 | 0.00899694 | 0.0039281 | -0.0146125 | -0.0214485 | -0.0174936 |
| num_bc_sats | -0.0103156 | 0.220138 | 0.220138 | 0.220116 | 0.0602925 | -0.0243449 | 0.209793 | 0.0688841 | 0.123213 | 0.00675165 | nan | 0.0754692 | -0.026932 | -0.13836 | 0.0642446 | 0.0642422 | 0.15623 | -0.0490637 | -0.0602441 | 0.6326 | -0.044667 | 0.279112 | -0.11625 | 0.412615 | 0.0205778 | -0.038223 | -0.0395794 | -0.0229321 | -0.029926 | 0.00178831 | 0.1967 | -0.0173969 | -0.00605558 | 0.000984645 | 0.00293162 | 0.0347752 | -0.0307332 | 0.332636 | 0.412793 | 0.206914 | -0.2125 | 0.0132135 | -0.0685984 | 0.0992322 | 0.335204 | -0.118107 | 0.484976 | -0.128802 | -0.00124331 | -0.000381842 | nan | nan | nan | nan | 0.0794778 | 0.163902 | -0.0108362 | 0.0780788 | -0.0238033 | -0.0627028 | 0.82043 | 0.666762 | 1 | 0.754651 | -0.00595212 | 0.749365 | 0.598343 | 0.637555 | 0.628036 | -0.00275183 | 0.00194617 | -0.0238053 | 0.267746 | 0.13986 | -0.133136 | -0.0644459 | 0.00332998 | 0.000639426 | -0.0159482 | -0.0355712 | -0.022067 |
| num_bc_tl | -0.0429805 | 0.199425 | 0.199425 | 0.199397 | 0.0500086 | -0.0745048 | 0.184321 | 0.0899515 | 0.128081 | -0.0233043 | nan | 0.0552756 | 0.0308758 | -0.263238 | 0.0737801 | 0.0737779 | 0.141211 | 0.0499351 | -0.0142455 | 0.5457 | -0.0180016 | 0.232235 | -0.150926 | 0.621164 | 0.0304233 | 0.0409552 | -0.0383825 | -0.0208823 | -0.0306313 | 0.0176981 | 0.191733 | -0.0285025 | 0.0258083 | 0.0392207 | 0.0289562 | 0.042446 | -0.0396905 | 0.300954 | 0.377464 | 0.182499 | -0.220668 | 0.049748 | 0.00226729 | 0.120394 | 0.328338 | -0.0580143 | 0.459524 | -0.153372 | 0.0293433 | 0.00136466 | nan | nan | nan | nan | 0.180937 | 0.116076 | 0.14231 | 0.079089 | 0.108153 | 0.0522256 | 0.595364 | 0.489308 | 0.754651 | 1 | 0.0427789 | 0.651757 | 0.836398 | 0.487024 | 0.54168 | 0.00525794 | 0.0146068 | 0.0110842 | 0.260238 | 0.0655869 | -0.145449 | -0.00593851 | -0.010921 | -0.00109308 | -0.00938568 | -0.0375617 | -0.0194202 |
| num_il_tl | -0.00498769 | 0.0729088 | 0.0729088 | 0.0728883 | 0.0653109 | 0.0154559 | 0.0598414 | 0.00275573 | 0.0929922 | -0.00598991 | nan | 0.157749 | 0.082474 | -0.0181363 | -0.0171835 | -0.0171868 | 0.0668369 | 0.120277 | -0.0135545 | 0.38191 | -0.0178215 | 0.0125949 | 0.0158534 | 0.69256 | 0.0147074 | 0.0925125 | -0.00106958 | 0.00943032 | 0.0145116 | 0.00768579 | 0.136626 | 0.630083 | 0.346195 | 0.470968 | 0.184509 | 0.579082 | 0.328646 | 0.0124119 | 0.0117532 | 0.0141902 | 0.256207 | 0.173357 | 0.304966 | 0.148112 | 0.2571 | 0.0448754 | -0.00451172 | 0.0135313 | 0.00961118 | -0.00188584 | nan | nan | nan | nan | 0.0846755 | -0.0184111 | 0.038387 | 0.0785013 | 0.0488154 | 0.145643 | -0.0194805 | 0.014075 | -0.00595212 | 0.0427789 | 1 | 0.0458202 | 0.0864782 | 0.0192931 | 0.383402 | 0.00119655 | 0.00493631 | 0.0727621 | 0.194377 | -0.00339816 | 0.0173363 | -0.00935613 | -0.0159769 | 0.00496934 | 0.00691429 | -0.00402448 | 0.000384541 |
| num_op_rev_tl | -0.0274384 | 0.160412 | 0.160412 | 0.160384 | 0.0507575 | 0.00648551 | 0.156451 | 0.0982971 | 0.0782448 | -0.00992501 | nan | 0.125151 | 0.0110338 | -0.169198 | 0.0134573 | 0.0134556 | 0.175681 | 0.0219983 | 0.0195522 | 0.840337 | 0.0107301 | 0.226513 | -0.201499 | 0.577508 | 0.0106175 | -0.00827835 | -0.0229003 | -0.00966986 | -0.0107856 | 0.0167498 | 0.277622 | -0.00685534 | 0.0223643 | 0.0505349 | 0.0271208 | 0.0427055 | -0.0140658 | 0.455826 | 0.584674 | 0.0895558 | -0.249764 | 0.0622347 | 0.0507981 | 0.159422 | 0.496488 | -0.200781 | 0.371504 | -0.124005 | 0.00677495 | 0.00155509 | nan | nan | nan | nan | 0.0840111 | 0.110806 | 0.0165989 | 0.112131 | 0.050745 | -0.0189137 | 0.650644 | 0.776673 | 0.749365 | 0.651757 | 0.0458202 | 1 | 0.794653 | 0.782106 | 0.839103 | 0.00199449 | 0.0154537 | -0.0225507 | 0.379347 | 0.130167 | -0.123017 | 0.0181009 | 0.000311432 | 0.00652393 | -0.00947076 | -0.0276161 | -0.0144157 |
| num_rev_accts | -0.0466677 | 0.163052 | 0.163052 | 0.163024 | 0.0501363 | -0.0537557 | 0.149827 | 0.11622 | 0.0984909 | -0.0268819 | nan | 0.097101 | 0.0751926 | -0.307887 | 0.0295203 | 0.0295188 | 0.172687 | 0.103071 | 0.035464 | 0.671288 | 0.0135757 | 0.215664 | -0.181344 | 0.761235 | 0.0249612 | 0.0716812 | -0.0268364 | -0.0121454 | -0.0173175 | 0.0284013 | 0.245263 | -0.0186272 | 0.0443078 | 0.0642075 | 0.0518723 | 0.0503017 | -0.0209398 | 0.376212 | 0.476877 | 0.116729 | -0.228374 | 0.0629156 | 0.112094 | 0.155754 | 0.42004 | -0.0945567 | 0.331844 | -0.123005 | 0.038421 | 0.00386748 | nan | nan | nan | nan | 0.211166 | 0.069867 | 0.136835 | 0.107386 | 0.165765 | 0.0642757 | 0.45893 | 0.574335 | 0.598343 | 0.836398 | 0.0864782 | 0.794653 | 1 | 0.570161 | 0.665393 | 0.00804668 | 0.0240981 | 0.0238512 | 0.329466 | 0.0533712 | -0.112284 | 0.0486757 | -0.0196402 | 0.00282971 | -0.00336047 | -0.0300403 | -0.0132694 |
| num_rev_tl_bal_gt_0 | -0.0602487 | 0.157129 | 0.157129 | 0.157086 | 0.0528589 | 0.0955494 | 0.165577 | 0.112049 | 0.077653 | -0.0363046 | nan | 0.184929 | -0.00182585 | -0.149252 | -0.195455 | -0.195455 | 0.129672 | 0.0165125 | 0.00290667 | 0.663984 | 0.00396216 | 0.308051 | 0.130536 | 0.405051 | -0.00742326 | 0.00823319 | -0.0160854 | -0.00449231 | 0.000179617 | 0.00383753 | 0.19597 | 0.00133266 | -0.0193469 | 0.00639011 | 0.0171077 | 0.0300477 | -0.0139502 | 0.356516 | 0.45121 | 0.11929 | -0.0026557 | 0.021789 | 0.0266929 | 0.0933912 | 0.363311 | -0.153349 | 0.122936 | 0.161241 | 0.00106656 | -0.000938581 | nan | nan | nan | nan | 0.0582078 | 0.105189 | 0.00477741 | 0.0857591 | 0.0252108 | -0.0157037 | 0.808971 | 0.975518 | 0.637555 | 0.487024 | 0.0192931 | 0.782106 | 0.570161 | 1 | 0.662889 | -0.00403072 | 0.00647178 | -0.0221875 | 0.273814 | 0.0898938 | 0.117669 | -0.00757742 | 0.010145 | 0.00735437 | -0.0124839 | -0.0186286 | -0.0158036 |
| num_sats | -0.0309258 | 0.176213 | 0.176213 | 0.176186 | 0.0707827 | 0.0108543 | 0.16713 | 0.0659711 | 0.125925 | -0.0163005 | nan | 0.195886 | 0.049985 | -0.133176 | 0.0240883 | 0.0240852 | 0.163325 | 0.0554331 | -0.0173759 | 0.998662 | -0.019072 | 0.215405 | -0.135513 | 0.708356 | 0.0153477 | 0.0135839 | -0.0177416 | 0.000173295 | -0.000113842 | 0.0115189 | 0.280231 | 0.517462 | 0.168107 | 0.239377 | 0.108734 | 0.34343 | 0.2131 | 0.367311 | 0.471632 | 0.106525 | -0.0110648 | 0.109912 | 0.101881 | 0.183727 | 0.510226 | -0.125071 | 0.309204 | -0.0750139 | 0.00553998 | -0.00123076 | nan | nan | nan | nan | 0.126177 | 0.0908174 | 0.0263332 | 0.127483 | 0.0581276 | 0.0322784 | 0.548949 | 0.656357 | 0.628036 | 0.54168 | 0.383402 | 0.839103 | 0.665393 | 0.662889 | 1 | 0.000914236 | 0.00987463 | 0.0141552 | 0.389719 | 0.10137 | -0.0758054 | -0.0192534 | -0.00727364 | 0.0113808 | -0.00345781 | -0.0229484 | -0.0119976 |
| num_tl_120dpd_2m | -0.00595114 | 0.0013363 | 0.0013363 | 0.00131546 | -0.00132722 | 0.00235276 | 0.00192625 | 0.00146513 | 0.00766741 | -0.00344101 | nan | -0.00494301 | 0.0472085 | -0.0106247 | -0.0180953 | -0.018095 | -0.00281417 | 0.0287721 | 0.00103694 | 0.00466632 | 0.00475399 | -0.00285345 | -0.0146131 | 0.00882538 | 0.000884112 | 0.0468141 | -0.00323222 | -0.0029595 | -0.0031668 | 0.40493 | -0.00618333 | 0.000432986 | -0.00431037 | -0.00534064 | 0.00112023 | 0.00390574 | -0.00287632 | -0.00472218 | -0.00401581 | -0.00680483 | -0.00707463 | -0.00292626 | 0.00140141 | -0.00495738 | -0.00651912 | 0.00738516 | 0.00566647 | -0.012314 | 0.0359113 | 0.367772 | nan | nan | nan | nan | 0.0097598 | -0.0011123 | 0.0124995 | -0.00225567 | 0.0164159 | 0.0322363 | -0.00628703 | -0.00478624 | -0.00275183 | 0.00525794 | 0.00119655 | 0.00199449 | 0.00804668 | -0.00403072 | 0.000914236 | 1 | 0.00108827 | 0.0728438 | -0.00668362 | -0.0202917 | -0.0136826 | -0.00218899 | 0.00807619 | -0.00224183 | -0.00236635 | -0.00320265 | -0.000394056 |
| num_tl_30dpd | -0.0102585 | 0.00312459 | 0.00312459 | 0.00310217 | -0.00163741 | 0.00567208 | 0.00431939 | 0.0105402 | 0.0105974 | -0.00769706 | nan | 0.001442 | 0.103201 | -0.0273597 | -0.0321727 | -0.0321722 | -0.00666443 | 0.0562023 | -0.00998001 | 0.0196159 | -0.00658831 | 0.0069155 | -0.0215178 | 0.0229275 | -0.000503387 | -0.00173777 | 0.000418642 | 0.0019704 | 0.000314872 | 0.795964 | -0.00669984 | 0.00593036 | -0.00401501 | -0.00540845 | 0.00523758 | 0.00531998 | -0.00583002 | -0.00491244 | -0.00452248 | 0.00441199 | -0.0217334 | -0.0065604 | 0.00234959 | -0.00562695 | -0.00613127 | 0.013113 | 0.0168384 | -0.0213142 | 0.000856444 | 0.0381949 | nan | nan | nan | nan | 0.0217753 | -0.0060109 | 0.045094 | -0.00016021 | 0.0572876 | -0.00596655 | 0.000230749 | 0.00764551 | 0.00194617 | 0.0146068 | 0.00493631 | 0.0154537 | 0.0240981 | 0.00647178 | 0.00987463 | 0.00108827 | 1 | 0.0047266 | -0.0061361 | -0.0376677 | -0.0183645 | -0.0144378 | 0.00583894 | -0.00191861 | -0.0017072 | -0.00297742 | -5.5049e-05 |
| num_tl_90g_dpd_24m | -0.00561178 | -0.0185283 | -0.0185283 | -0.018561 | -0.0113272 | 0.0198795 | -0.0139407 | -0.00294973 | 0.00193351 | -0.00695552 | nan | -0.0118116 | 0.665355 | -0.0329759 | -0.101024 | -0.101022 | 0.0277039 | 0.152675 | -0.0169895 | 0.0159108 | -0.0117064 | -0.028822 | -0.00928085 | 0.0656149 | -0.0117098 | 0.271122 | -0.00540271 | -0.00378128 | -0.00559278 | 0.0583675 | 0.0095132 | 0.0614521 | -0.00208664 | -0.022563 | 0.0163838 | 0.0370412 | 0.0332349 | -0.000297686 | -0.0229226 | -0.0462697 | 0.0324189 | 0.00873659 | -0.00263983 | 0.0160847 | -0.0341718 | 0.00446792 | -0.0442112 | -0.0120727 | 0.227076 | 0.0505135 | nan | nan | nan | nan | 0.0132967 | -0.0106523 | 0.114447 | 0.00881176 | 0.116126 | 0.317129 | -0.029503 | -0.0145671 | -0.0238053 | 0.0110842 | 0.0727621 | -0.0225507 | 0.0238512 | -0.0221875 | 0.0141552 | 0.0728438 | 0.0047266 | 1 | -0.00531493 | -0.266931 | -0.0138684 | -0.0247753 | 0.00218613 | -0.00612248 | -0.00436704 | -0.00390932 | 0.00860913 |
| num_tl_op_past_12m | -0.0179269 | -0.012524 | -0.012524 | -0.0125626 | 0.0181777 | 0.214301 | 0.0132499 | 0.0352015 | 0.0520409 | -0.0211688 | nan | 0.0566104 | -0.0285821 | -0.00379716 | -0.0953209 | -0.0953241 | 0.353596 | 0.0539345 | 0.0987125 | 0.391381 | 0.0823809 | -0.0157294 | -0.206963 | 0.354305 | -0.053438 | 0.097447 | -0.0241895 | -0.0178815 | -0.0170224 | -0.00717039 | 0.720908 | 0.12235 | 0.561705 | 0.431285 | 0.0683615 | 0.156345 | 0.216616 | 0.837077 | 0.654754 | -0.0968394 | -0.0402328 | 0.228857 | 0.121665 | 0.448994 | 0.771942 | -0.0495513 | 0.117169 | -0.171062 | -0.00218708 | -0.00524767 | nan | nan | nan | nan | 0.0666973 | 0.0373853 | 0.0341549 | 0.211114 | 0.0339703 | 0.0752611 | 0.197673 | 0.286416 | 0.267746 | 0.260238 | 0.194377 | 0.379347 | 0.329466 | 0.273814 | 0.389719 | -0.00668362 | -0.0061361 | -0.00531493 | 1 | 0.0250933 | -0.16699 | 0.0873331 | 0.0197826 | -0.0148852 | -0.00887584 | -0.0251435 | -0.00906105 |
| pct_tl_nvr_dlq | -0.00156829 | 0.0702641 | 0.0702641 | 0.0703008 | 0.0383303 | -0.0472754 | 0.0553822 | -0.0166497 | 0.000633011 | 0.00721334 | nan | 0.0640695 | -0.440008 | 0.0884751 | 0.296871 | 0.296867 | -0.022165 | -0.608835 | 0.0368363 | 0.100041 | 0.0121686 | 0.0965034 | -0.0403029 | 0.0289515 | 0.0182647 | -0.559342 | 0.00370765 | 0.0012872 | 0.00900865 | -0.0489672 | 0.00667259 | -0.0109671 | 0.0264624 | 0.0651893 | -0.0239216 | 0.00849149 | -0.0211727 | 0.00695222 | 0.0397782 | 0.128772 | -0.0762987 | -0.017259 | 0.0470462 | -0.0192305 | 0.0729495 | -0.0435262 | 0.165385 | -0.0324937 | -0.0806482 | -0.0171228 | nan | nan | nan | nan | -0.0144858 | 0.0315371 | -0.520245 | -0.017555 | -0.550831 | -0.589166 | 0.109193 | 0.084511 | 0.13986 | 0.0655869 | -0.00339816 | 0.130167 | 0.0533712 | 0.0898938 | 0.10137 | -0.0202917 | -0.0376677 | -0.266931 | 0.0250933 | 1 | -0.0186247 | 0.0854826 | -0.0284664 | 0.0161255 | 0.00325997 | -0.000611669 | -0.0305037 |
| percent_bc_gt_75 | -0.0760892 | 0.04447 | 0.04447 | 0.0444573 | 0.0400646 | 0.205924 | 0.0651471 | 0.0374231 | 0.000613847 | -0.0597402 | nan | 0.133691 | -0.00673088 | -0.0353742 | -0.399495 | -0.399491 | -0.0854127 | 0.0043547 | -0.0300964 | -0.0750634 | -0.0295045 | 0.157006 | 0.723753 | -0.0646244 | -0.0279433 | -0.00136059 | 0.0252186 | 0.0224391 | 0.0338727 | -0.0262401 | -0.151688 | 0.0425085 | -0.0685178 | -0.0520546 | 0.0133864 | 0.0168712 | 0.00629931 | -0.155791 | -0.153979 | 0.217872 | 0.478858 | -0.0614376 | -0.000464966 | -0.103555 | -0.154705 | 0.0709961 | -0.411689 | 0.845272 | -0.0101618 | -0.00763145 | nan | nan | nan | nan | 0.0126638 | 0.116542 | -0.00849793 | -0.0454136 | -0.00396584 | -0.0238768 | 0.0581039 | 0.111197 | -0.133136 | -0.145449 | 0.0173363 | -0.123017 | -0.112284 | 0.117669 | -0.0758054 | -0.0136826 | -0.0183645 | -0.0138684 | -0.16699 | -0.0186247 | 1 | -0.0271813 | -0.0128984 | 0.0131655 | 0.000574869 | 0.0229265 | -0.00515201 |
| pub_rec_bankruptcies | 0.00648718 | -0.0722752 | -0.0722752 | -0.0723112 | -0.0025567 | 0.0569179 | -0.0633205 | 0.00503536 | -0.0367303 | 0.0029982 | nan | -0.0127161 | -0.064268 | -0.0560401 | -0.206976 | -0.206973 | 0.0855617 | -0.0888289 | 0.776127 | -0.0134098 | 0.654163 | -0.107666 | -0.0690148 | 0.0234767 | -0.0149067 | -0.0395159 | 0.00528841 | -0.0023287 | 0.00289668 | -0.0115422 | 0.0558972 | -0.0286686 | 0.0463047 | 0.0526413 | 0.0212014 | -0.0283581 | 0.0326699 | 0.0796004 | 0.111202 | -0.129389 | -0.0158144 | 0.0731705 | 0.0172131 | 0.104239 | 0.114637 | -0.0807372 | -0.0997203 | -0.018847 | -0.0154123 | -0.0029183 | nan | nan | nan | nan | -0.0174074 | 0.00425672 | -0.0823344 | 0.0656321 | -0.0982129 | -0.0468898 | -0.056417 | -0.00444145 | -0.0644459 | -0.00593851 | -0.00935613 | 0.0181009 | 0.0486757 | -0.00757742 | -0.0192534 | -0.00218899 | -0.0144378 | -0.0247753 | 0.0873331 | 0.0854826 | -0.0271813 | 1 | 0.0347124 | -0.00917239 | -3.80428e-05 | -0.00107613 | -0.00949286 |
| tax_liens | -0.0176822 | 0.0132666 | 0.0132666 | 0.0132395 | -0.00782063 | 0.0151899 | 0.018998 | 0.00531877 | 0.0350064 | -0.0132262 | nan | -0.0257022 | 0.00593368 | -0.0281559 | -0.0617305 | -0.0617298 | 0.0181919 | 0.0265877 | 0.299501 | -0.0073593 | 0.686977 | -0.010275 | -0.00643908 | -0.0252072 | -0.00594051 | 0.019823 | -0.00976738 | -0.00788305 | -0.0104434 | 0.00792196 | 0.0152902 | -0.0122823 | 0.00654293 | 0.00415751 | 0.00247218 | 0.000789849 | -0.00220551 | 0.0217304 | 0.0252402 | -0.0217796 | -0.00264618 | 0.0202849 | -0.014358 | 0.0228439 | 0.0208618 | -0.00148414 | -0.0197553 | -0.00370829 | -0.000991514 | 0.0054966 | nan | nan | nan | nan | -0.0178562 | 0.00565532 | 0.0113915 | 0.0119967 | 0.00937857 | 0.00973285 | 0.0133264 | 0.00899694 | 0.00332998 | -0.010921 | -0.0159769 | 0.000311432 | -0.0196402 | 0.010145 | -0.00727364 | 0.00807619 | 0.00583894 | 0.00218613 | 0.0197826 | -0.0284664 | -0.0128984 | 0.0347124 | 1 | -0.00935712 | -0.0083721 | -0.00923122 | -0.00434655 |
| revol_bal_joint | 0.173537 | 0.0917166 | 0.0917166 | 0.0917409 | 0.0546446 | 0.0305633 | 0.0794495 | -0.0640999 | -0.0211773 | 0.153795 | nan | 0.157884 | -0.00670418 | -0.00784146 | 0.0357951 | 0.0357942 | -0.0193547 | -0.0151579 | -0.0158114 | 0.0107744 | -0.0161148 | 0.0350645 | 0.0198315 | 0.0101585 | 0.0176648 | -0.020143 | 0.572076 | 0.634224 | 0.610817 | -0.00329687 | -0.0126559 | 0.00421186 | -0.00521119 | -0.00377276 | -0.000141324 | 0.0222411 | -0.0121975 | -0.0186303 | -0.020129 | 0.0469201 | 0.00676926 | 0.00407757 | 0.0245087 | -0.00750618 | -0.0135357 | 0.0469711 | 0.0149981 | 0.0106302 | -0.0034651 | -0.00196265 | nan | nan | nan | nan | 0.0361079 | -0.00607725 | -0.0135996 | -0.00719881 | -0.0130986 | -0.0138636 | 0.00282292 | 0.0039281 | 0.000639426 | -0.00109308 | 0.00496934 | 0.00652393 | 0.00282971 | 0.00735437 | 0.0113808 | -0.00224183 | -0.00191861 | -0.00612248 | -0.0148852 | 0.0161255 | 0.0131655 | -0.00917239 | -0.00935712 | 1 | 0.629213 | 0.740915 | 0.375035 |
| sec_app_mort_acc | 0.150042 | 0.0654644 | 0.0654644 | 0.0654809 | 0.0462316 | 0.00964099 | 0.0522894 | -0.0570702 | -0.0262688 | 0.135667 | nan | 0.12818 | -0.00149503 | -0.0099418 | 0.0349613 | 0.0349624 | -0.0154953 | -0.00237543 | -0.00506234 | -0.0037312 | -0.0083866 | 0.00766717 | 0.00492423 | 0.0147019 | 0.0160926 | -0.00844678 | 0.504644 | 0.525145 | 0.473685 | -0.00300911 | -0.00728085 | -0.00247352 | -0.00469154 | -0.00241752 | 0.00206934 | 0.0140907 | -0.0122938 | -0.0179965 | -0.0217354 | 0.0172944 | -0.00215361 | 0.0118063 | 0.0345538 | 0.00157327 | -0.00818275 | 0.0618051 | 0.00844124 | -0.00364648 | -0.00369874 | -0.00178863 | nan | nan | nan | nan | 0.0868426 | -0.0114841 | -0.00428139 | 0.00214206 | -0.0033705 | -0.00630334 | -0.016675 | -0.0146125 | -0.0159482 | -0.00938568 | 0.00691429 | -0.00947076 | -0.00336047 | -0.0124839 | -0.00345781 | -0.00236635 | -0.0017072 | -0.00436704 | -0.00887584 | 0.00325997 | 0.000574869 | -3.80428e-05 | -0.0083721 | 0.629213 | 1 | 0.598185 | 0.35519 |
| sec_app_revol_util | 0.201232 | 0.0651616 | 0.0651616 | 0.065188 | 0.0512744 | 0.0466987 | 0.0577813 | -0.0825761 | -0.0391232 | 0.181264 | nan | 0.156272 | -0.00247188 | 0.0119592 | 0.0193274 | 0.0193286 | -0.021927 | -0.00673003 | -0.00774973 | -0.0234721 | -0.0103497 | 0.00317158 | 0.0295645 | -0.020617 | 0.0132245 | -0.0127033 | 0.674253 | 0.628761 | 0.655014 | -0.00518892 | -0.0204226 | -0.00348556 | -0.0112569 | -0.0122527 | -0.00470371 | 0.00881707 | -0.0133814 | -0.0269882 | -0.0307799 | 0.00881723 | 0.016229 | 0.00628767 | 0.0207548 | -0.00676538 | -0.0270999 | 0.0416053 | -0.0193241 | 0.0180859 | -0.00350607 | -0.00237695 | nan | nan | nan | nan | 0.0186783 | -0.0225751 | -0.00994155 | -0.00855814 | -0.00786351 | -0.00817286 | -0.0271403 | -0.0214485 | -0.0355712 | -0.0375617 | -0.00402448 | -0.0276161 | -0.0300403 | -0.0186286 | -0.0229484 | -0.00320265 | -0.00297742 | -0.00390932 | -0.0251435 | -0.000611669 | 0.0229265 | -0.00107613 | -0.00923122 | 0.740915 | 0.598185 | 1 | 0.543998 |
| sec_app_mths_since_last_major_derog | 0.134191 | 0.025622 | 0.025622 | 0.0256408 | 0.0269252 | 0.0322549 | 0.0232078 | -0.0539475 | -0.0272948 | 0.120069 | nan | 0.0887798 | 0.0117696 | 0.0037996 | 0.00648239 | 0.00648387 | -0.00925314 | 0.0257454 | -0.0118531 | -0.0122987 | -0.0104793 | -0.0122944 | -0.00544781 | -0.0080504 | 0.00580819 | 0.0311284 | 0.446625 | 0.409268 | 0.406984 | -0.00104258 | -0.00799011 | -0.000702324 | -0.00325605 | -0.00416827 | -0.00408784 | 0.00681597 | -0.00771851 | -0.0106544 | -0.0121039 | -0.0134969 | -0.00191463 | 0.0133634 | 0.00824441 | 0.00452644 | -0.0106693 | 0.0160454 | -0.00872533 | -0.00833916 | 0.0031394 | -0.00176629 | nan | nan | nan | nan | 0.00742479 | -0.014197 | 0.016833 | -0.000433285 | 0.0195779 | 0.0199888 | -0.0203711 | -0.0174936 | -0.022067 | -0.0194202 | 0.000384541 | -0.0144157 | -0.0132694 | -0.0158036 | -0.0119976 | -0.000394056 | -5.5049e-05 | 0.00860913 | -0.00906105 | -0.0305037 | -0.00515201 | -0.00949286 | -0.00434655 | 0.375035 | 0.35519 | 0.543998 | 1 |
NaN values in every column, which doesn't seem very useful for anything.mo_sin_old_il_acctmo_sin_old_rev_tl_opmo_sin_rcnt_rev_tl_opmo_sin_rcnt_tlpmnt_planmake_scatterplot (below) can be used to have a closer look at some of the variables that showed as highly correlated in the above analysis. Based on the correlation analysis, we therefore also decided to remove the following columns:funded_amntfunded_amnt_invnum_satsapplication_typenum_actv_rev_tldef make_scatterplot(x, y):
'''Plots a scatter plot graph
Inputs:
x, y: pd.Series objects representing the x and y values to plot
'''
fig = plt.figure(figsize=(8,5))
plt.scatter(x, y, alpha=0.8)
plt.title('Compare two variables', fontsize=16)
plt.xlabel(x.name, fontsize=14)
plt.ylabel(y.name, fontsize=14)
plt.legend(loc='best')
plt.show()
Now we encode the ordinal and nominal categorical variables and drop the last two xx for zip_code:
object_columns = []
for column in original_df:
if original_df[column].dtype == 'object':
object_columns.append(column)
object_columns
['grade', 'sub_grade', 'home_ownership', 'verification_status', 'loan_status', 'purpose', 'zip_code', 'addr_state']
# One function to do all of the above operations
def data_prep(df):
'''Returns a cleaned up dataframe as basis for the EDA analysis
Input:
df: the pd.DataFrame object
Returns:
clean_df: pd.DataFrame object
'''
clean_df = df.copy()
# Definitions
cols_to_remove = ['mo_sin_old_il_acct', 'mo_sin_old_rev_tl_op', 'mo_sin_rcnt_rev_tl_op',
'mo_sin_rcnt_tl', 'funded_amnt', 'funded_amnt_inv', 'num_sats',
'application_type', 'num_actv_rev_tl', 'pymnt_plan']
nominal_columns = ['home_ownership', 'verification_status', 'purpose', 'addr_state']
prefixes = ['home', 'verify', 'purp', 'state']
# Drop additional uninformative columns
clean_df = clean_df.drop(columns=cols_to_remove)
# strip xx from zip code
clean_df["zip_code"] = [x.strip("xx") for x in clean_df["zip_code"].astype(str)]
clean_df["zip_code"] = clean_df["zip_code"].astype(int)
# exclude zip code from set of predictors for analysis
# to be added back in later for checking discrimination...
clean_df = clean_df.drop(columns='zip_code')
# ordinal columns are encoded as numerical values, as there is an order
clean_df["grade"].replace({"A": 1, "B": 2, "C": 3, "D": 4, "E": 5, "F": 6, "G": 7}, inplace = True)
clean_df["sub_grade"].replace({"A1": 1, "A2": 2, "A3": 3, "A4": 4, "A5": 5,
"B1": 6, "B2": 7, "B3": 8, "B4": 9, "B5": 10,
"C1": 11, "C2": 12, "C3": 13, "C4": 14, "C5": 15,
"D1": 16, "D2": 17, "D3": 18, "D4": 19, "D5": 20,
"E1": 21, "E2": 22, "E3": 23, "E4": 24, "E5": 25,
"F1": 26, "F2": 27, "F3": 28, "F4": 29, "F5": 30,
"G1": 31, "G2": 32, "G3": 33, "G4": 34, "G5": 35}, inplace = True)
# nominal columns are encoded via hot encoding by adding more columns
clean_df = pd.get_dummies(clean_df, columns=nominal_columns, prefix=prefixes, drop_first=True)
return clean_df
df_all = data_prep(original_df)
df_all.shape
(334109, 141)
Since we have imbalanced classes that can cause misleading assessment of model performance, we resample the classes here. We also take the opportunity to reduce the dataset size in the initial stages, so that different model specifications can be tested faster.
# Check balance of target values - it is unbalanced
df_all["loan_status"].value_counts().to_frame()
| loan_status | |
|---|---|
| Fully Paid | 255116 |
| Charged Off | 78993 |
# Downsample and create balanced classes
def balance_classes(df, n_samples):
# Define majority and minority classes, 1 = Fully paid, 0 = Charged off
df_majority = df[df["loan_status"] == "Fully Paid"]
df_minority = df[df["loan_status"] == "Charged Off"]
# Downsample majority class
df_majority_downsampled = resample(df_majority,
replace = False, # sample without replacement
n_samples = n_samples,
random_state = 1) # set random seed for reproducability
# Downsample minority class
df_minority_downsampled = resample(df_minority,
replace = False,
n_samples = n_samples,
random_state = 1)
# Recombine
df_downsampled = pd.concat([df_majority_downsampled, df_minority_downsampled])
return df_downsampled
# Define how many samples of each we want
n_samples = 10000
df = balance_classes(df_all, n_samples)
df["loan_status"].value_counts().to_frame()
| loan_status | |
|---|---|
| Charged Off | 10000 |
| Fully Paid | 10000 |
display(df.describe())
| id | loan_amnt | term | int_rate | installment | grade | sub_grade | emp_length | annual_inc | issue_d | dti | delinq_2yrs | earliest_cr_line | fico_range_low | fico_range_high | inq_last_6mths | mths_since_last_delinq | mths_since_last_record | open_acc | pub_rec | revol_bal | revol_util | total_acc | initial_list_status | mths_since_last_major_derog | annual_inc_joint | dti_joint | acc_now_delinq | open_acc_6m | open_act_il | open_il_12m | open_il_24m | mths_since_rcnt_il | total_bal_il | il_util | open_rv_12m | open_rv_24m | max_bal_bc | all_util | inq_fi | total_cu_tl | inq_last_12m | acc_open_past_24mths | avg_cur_bal | bc_open_to_buy | bc_util | chargeoff_within_12_mths | delinq_amnt | mort_acc | mths_since_recent_bc | mths_since_recent_bc_dlq | mths_since_recent_inq | mths_since_recent_revol_delinq | num_accts_ever_120_pd | num_actv_bc_tl | num_bc_sats | num_bc_tl | num_il_tl | num_op_rev_tl | num_rev_accts | num_rev_tl_bal_gt_0 | num_tl_120dpd_2m | num_tl_30dpd | num_tl_90g_dpd_24m | num_tl_op_past_12m | pct_tl_nvr_dlq | percent_bc_gt_75 | pub_rec_bankruptcies | tax_liens | revol_bal_joint | sec_app_mort_acc | sec_app_revol_util | sec_app_mths_since_last_major_derog | home_MORTGAGE | home_NONE | home_OWN | home_RENT | verify_Source Verified | verify_Verified | purp_credit_card | purp_debt_consolidation | purp_home_improvement | purp_house | purp_major_purchase | purp_medical | purp_moving | purp_other | purp_renewable_energy | purp_small_business | purp_vacation | purp_wedding | state_AL | state_AR | state_AZ | state_CA | state_CO | state_CT | state_DC | state_DE | state_FL | state_GA | state_HI | state_ID | state_IL | state_IN | state_KS | state_KY | state_LA | state_MA | state_MD | state_ME | state_MI | state_MN | state_MO | state_MS | state_MT | state_NC | state_ND | state_NE | state_NH | state_NJ | state_NM | state_NV | state_NY | state_OH | state_OK | state_OR | state_PA | state_RI | state_SC | state_SD | state_TN | state_TX | state_UT | state_VA | state_VT | state_WA | state_WI | state_WV | state_WY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2.000000e+04 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 2.000000e+04 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.00000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.00000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.00000 | 20000.000000 | 20000.0000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.00000 | 20000.000000 | 20000.000000 | 20000.00000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.00000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.0 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.00000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.00000 | 20000.000000 | 20000.00000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.00000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.000000 | 20000.00000 | 20000.00000 |
| mean | 8.995035e+07 | 14845.695000 | 0.262550 | 14.527035 | 459.439889 | 3.029100 | 13.132300 | 5.504275 | 7.711921e+04 | 2016.316400 | 19.428785 | 0.354800 | 2000.068650 | 693.31600 | 697.316150 | 0.647250 | 0.527850 | 0.210200 | 11.869650 | 0.266850 | 15585.475900 | 49.242785 | 24.716200 | 0.740250 | 0.299300 | 3802.495215 | 0.688934 | 0.007000 | 1.108800 | 2.856100 | 0.837950 | 1.836950 | 0.973600 | 35986.670600 | 63.984300 | 1.492350 | 3.204150 | 5390.908650 | 60.317750 | 1.150700 | 1.649400 | 2.472950 | 5.317800 | 12935.132350 | 10274.992550 | 56.271810 | 0.008900 | 22.014000 | 1.479250 | 0.988350 | 0.25425 | 0.923100 | 0.360900 | 0.568700 | 3.609100 | 4.767800 | 7.636500 | 8.88310 | 8.331300 | 14.1224 | 5.547100 | 0.000750 | 0.004350 | 0.094450 | 2.472500 | 93.738685 | 41.245695 | 0.165000 | 0.065550 | 593.181250 | 0.032750 | 1.173845 | 0.008250 | 0.462800 | 0.000050 | 0.123400 | 0.41280 | 0.414300 | 0.317100 | 0.19315 | 0.580150 | 0.073500 | 0.005400 | 0.024150 | 0.014450 | 0.009050 | 0.06930 | 0.000850 | 0.012700 | 0.007900 | 0.0 | 0.011600 | 0.008550 | 0.025000 | 0.142600 | 0.02075 | 0.013850 | 0.002050 | 0.003550 | 0.078100 | 0.031900 | 0.00550 | 0.003500 | 0.03630 | 0.018050 | 0.007700 | 0.009350 | 0.011400 | 0.02325 | 0.022850 | 0.002850 | 0.025000 | 0.018150 | 0.014600 | 0.006150 | 0.002550 | 0.030300 | 0.002450 | 0.005450 | 0.003950 | 0.034750 | 0.006600 | 0.014900 | 0.080750 | 0.032050 | 0.009400 | 0.010200 | 0.033100 | 0.004300 | 0.010750 | 0.002250 | 0.016350 | 0.084300 | 0.007500 | 0.025750 | 0.001850 | 0.019450 | 0.013000 | 0.00155 | 0.00155 |
| std | 1.582901e+07 | 9175.396697 | 0.440031 | 5.564589 | 285.921531 | 1.344263 | 6.717764 | 3.848871 | 7.514298e+04 | 0.465083 | 11.036975 | 0.949717 | 7.652501 | 31.21859 | 31.219321 | 0.912228 | 0.499236 | 0.407461 | 5.732146 | 0.661712 | 21401.815729 | 24.738491 | 12.108133 | 0.438508 | 0.457963 | 21979.555941 | 3.892103 | 0.092474 | 1.242191 | 3.026838 | 1.020462 | 1.722386 | 0.160326 | 41740.899612 | 31.802208 | 1.625753 | 2.823521 | 5678.146806 | 20.640692 | 1.648886 | 2.858179 | 2.629261 | 3.487436 | 15427.656235 | 14876.425265 | 29.491357 | 0.105931 | 890.362325 | 1.814479 | 0.107307 | 0.43545 | 0.266439 | 0.480274 | 1.449547 | 2.318095 | 3.077362 | 4.673239 | 7.52271 | 4.768383 | 8.1751 | 3.325122 | 0.027377 | 0.069508 | 0.519849 | 2.030751 | 9.114103 | 36.310992 | 0.411198 | 0.446389 | 5510.168982 | 0.347683 | 9.105016 | 0.090456 | 0.498627 | 0.007071 | 0.328904 | 0.49235 | 0.492613 | 0.465358 | 0.39478 | 0.493547 | 0.260962 | 0.073288 | 0.153519 | 0.119339 | 0.094702 | 0.25397 | 0.029143 | 0.111979 | 0.088532 | 0.0 | 0.107079 | 0.092072 | 0.156129 | 0.349673 | 0.14255 | 0.116871 | 0.045232 | 0.059478 | 0.268336 | 0.175738 | 0.07396 | 0.059059 | 0.18704 | 0.133136 | 0.087413 | 0.096245 | 0.106163 | 0.15070 | 0.149429 | 0.053311 | 0.156129 | 0.133497 | 0.119948 | 0.078182 | 0.050434 | 0.171416 | 0.049438 | 0.073625 | 0.062726 | 0.183151 | 0.080974 | 0.121156 | 0.272458 | 0.176137 | 0.096499 | 0.100481 | 0.178902 | 0.065435 | 0.103126 | 0.047382 | 0.126821 | 0.277844 | 0.086279 | 0.158393 | 0.042973 | 0.138104 | 0.113277 | 0.03934 | 0.03934 |
| min | 6.510400e+04 | 1000.000000 | 0.000000 | 5.320000 | 30.650000 | 1.000000 | 1.000000 | 0.000000 | 0.000000e+00 | 2016.000000 | 0.000000 | 0.000000 | 1956.000000 | 660.00000 | 664.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 2.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 15.400000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.00000 |
| 25% | 7.605364e+07 | 8000.000000 | 0.000000 | 10.750000 | 252.542500 | 2.000000 | 8.000000 | 2.000000 | 4.600000e+04 | 2016.000000 | 12.520000 | 0.000000 | 1996.000000 | 670.00000 | 674.000000 | 0.000000 | 0.000000 | 0.000000 | 8.000000 | 0.000000 | 5537.750000 | 30.300000 | 16.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 10432.500000 | 50.000000 | 0.000000 | 1.000000 | 2167.750000 | 47.000000 | 0.000000 | 0.000000 | 1.000000 | 3.000000 | 3052.750000 | 1601.750000 | 32.900000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.00000 | 1.000000 | 0.000000 | 0.000000 | 2.000000 | 3.000000 | 4.000000 | 4.00000 | 5.000000 | 8.0000 | 3.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 90.900000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.00000 |
| 50% | 8.821778e+07 | 12500.000000 | 0.000000 | 13.590000 | 382.680000 | 3.000000 | 12.000000 | 5.000000 | 6.500000e+04 | 2016.000000 | 18.670000 | 0.000000 | 2002.000000 | 685.00000 | 689.000000 | 0.000000 | 1.000000 | 0.000000 | 11.000000 | 0.000000 | 10414.500000 | 49.000000 | 23.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 2.000000 | 1.000000 | 1.000000 | 1.000000 | 24998.500000 | 73.000000 | 1.000000 | 3.000000 | 4029.500000 | 62.000000 | 1.000000 | 0.000000 | 2.000000 | 5.000000 | 6801.500000 | 5053.500000 | 58.500000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 0.00000 | 1.000000 | 0.000000 | 0.000000 | 3.000000 | 4.000000 | 7.000000 | 7.00000 | 7.000000 | 12.0000 | 5.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 97.100000 | 33.300000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.00000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.00000 |
| 75% | 9.956666e+07 | 20000.000000 | 1.000000 | 17.990000 | 612.942500 | 4.000000 | 17.000000 | 10.000000 | 9.100000e+04 | 2017.000000 | 25.540000 | 0.000000 | 2005.000000 | 710.00000 | 714.000000 | 1.000000 | 1.000000 | 0.000000 | 15.000000 | 0.000000 | 18528.250000 | 67.800000 | 31.250000 | 1.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 3.000000 | 1.000000 | 3.000000 | 1.000000 | 46629.250000 | 87.000000 | 2.000000 | 4.000000 | 6968.000000 | 75.000000 | 2.000000 | 2.000000 | 3.000000 | 7.000000 | 17941.750000 | 12665.500000 | 82.100000 | 0.000000 | 0.000000 | 2.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 5.000000 | 6.000000 | 10.000000 | 12.00000 | 11.000000 | 18.0000 | 7.000000 | 0.000000 | 0.000000 | 0.000000 | 3.000000 | 100.000000 | 66.700000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 1.00000 | 1.000000 | 1.000000 | 0.00000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.00000 |
| max | 1.264077e+08 | 40000.000000 | 1.000000 | 30.990000 | 1569.110000 | 7.000000 | 35.000000 | 10.000000 | 6.693021e+06 | 2017.000000 | 490.070000 | 17.000000 | 2014.000000 | 845.00000 | 850.000000 | 5.000000 | 1.000000 | 1.000000 | 71.000000 | 28.000000 | 598769.000000 | 162.000000 | 109.000000 | 1.000000 | 1.000000 | 434000.000000 | 61.280000 | 4.000000 | 14.000000 | 36.000000 | 11.000000 | 20.000000 | 1.000000 | 655706.000000 | 268.000000 | 20.000000 | 45.000000 | 361299.000000 | 162.000000 | 28.000000 | 48.000000 | 29.000000 | 49.000000 | 208984.000000 | 231418.000000 | 182.300000 | 4.000000 | 65000.000000 | 17.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 34.000000 | 23.000000 | 36.000000 | 51.000000 | 80.00000 | 61.000000 | 92.0000 | 36.000000 | 1.000000 | 2.000000 | 16.000000 | 23.000000 | 100.000000 | 100.000000 | 5.000000 | 27.000000 | 184999.000000 | 9.000000 | 110.700000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 0.0 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.00000 |
Since we will be using models that are sensitive to scale, we need to scale the data. This is done in a function for reproducability.
# Function to generate train test splits and scale the data
def get_train_test(df, test_size = 0.2):
nonbinary_columns = ['id','loan_amnt','int_rate','installment','grade','sub_grade','emp_length',
'annual_inc','issue_d','dti', 'delinq_2yrs','earliest_cr_line','inq_last_6mths',
'open_acc','pub_rec','revol_bal','revol_util','total_acc', 'annual_inc_joint',
'dti_joint','acc_now_delinq','open_acc_6m','open_act_il', 'open_il_12m',
'open_il_24m', 'total_bal_il','il_util','open_rv_12m','open_rv_24m','max_bal_bc',
'all_util','inq_fi','total_cu_tl','inq_last_12m','acc_open_past_24mths',
'avg_cur_bal','bc_open_to_buy','bc_util','chargeoff_within_12_mths',
'delinq_amnt','mort_acc','num_accts_ever_120_pd', 'num_actv_bc_tl',
'num_bc_sats','num_bc_tl', 'num_il_tl', 'num_op_rev_tl', 'num_rev_accts',
'num_rev_tl_bal_gt_0', 'num_tl_120dpd_2m', 'num_tl_30dpd', 'num_tl_90g_dpd_24m',
'num_tl_op_past_12m', 'pct_tl_nvr_dlq', 'percent_bc_gt_75', 'pub_rec_bankruptcies',
'tax_liens', 'revol_bal_joint', 'sec_app_mort_acc', 'sec_app_revol_util',
'sec_app_mths_since_last_major_derog']
result = df.copy()
# Train test split
data_train, data_test = train_test_split(result, test_size = test_size, random_state = 1)
# Split into x and y
X_train = data_train.iloc[:, data_train.columns != 'loan_status']
y_train = data_train['loan_status']
X_test = data_test.iloc[:, data_test.columns != 'loan_status']
y_test = data_test['loan_status']
# Incorporate scaling here so that we can use it consistently across all models
scaler = StandardScaler()
X_train[nonbinary_columns] = scaler.fit_transform(X_train[nonbinary_columns])
X_test[nonbinary_columns] = scaler.transform(X_test[nonbinary_columns])
return X_train, y_train, X_test, y_test
# Create scaled train and test sets
X_train, y_train, X_test, y_test = get_train_test(df)
/Users/rs/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:617: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler. /Users/rs/anaconda3/lib/python3.6/site-packages/sklearn/base.py:462: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler. /Users/rs/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:30: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.
# Check that data has scaled correctly
display(X_train.describe())
| id | loan_amnt | term | int_rate | installment | grade | sub_grade | emp_length | annual_inc | issue_d | dti | delinq_2yrs | earliest_cr_line | fico_range_low | fico_range_high | inq_last_6mths | mths_since_last_delinq | mths_since_last_record | open_acc | pub_rec | revol_bal | revol_util | total_acc | initial_list_status | mths_since_last_major_derog | annual_inc_joint | dti_joint | acc_now_delinq | open_acc_6m | open_act_il | open_il_12m | open_il_24m | mths_since_rcnt_il | total_bal_il | il_util | open_rv_12m | open_rv_24m | max_bal_bc | all_util | inq_fi | total_cu_tl | inq_last_12m | acc_open_past_24mths | avg_cur_bal | bc_open_to_buy | bc_util | chargeoff_within_12_mths | delinq_amnt | mort_acc | mths_since_recent_bc | mths_since_recent_bc_dlq | mths_since_recent_inq | mths_since_recent_revol_delinq | num_accts_ever_120_pd | num_actv_bc_tl | num_bc_sats | num_bc_tl | num_il_tl | num_op_rev_tl | num_rev_accts | num_rev_tl_bal_gt_0 | num_tl_120dpd_2m | num_tl_30dpd | num_tl_90g_dpd_24m | num_tl_op_past_12m | pct_tl_nvr_dlq | percent_bc_gt_75 | pub_rec_bankruptcies | tax_liens | revol_bal_joint | sec_app_mort_acc | sec_app_revol_util | sec_app_mths_since_last_major_derog | home_MORTGAGE | home_NONE | home_OWN | home_RENT | verify_Source Verified | verify_Verified | purp_credit_card | purp_debt_consolidation | purp_home_improvement | purp_house | purp_major_purchase | purp_medical | purp_moving | purp_other | purp_renewable_energy | purp_small_business | purp_vacation | purp_wedding | state_AL | state_AR | state_AZ | state_CA | state_CO | state_CT | state_DC | state_DE | state_FL | state_GA | state_HI | state_ID | state_IL | state_IN | state_KS | state_KY | state_LA | state_MA | state_MD | state_ME | state_MI | state_MN | state_MO | state_MS | state_MT | state_NC | state_ND | state_NE | state_NH | state_NJ | state_NM | state_NV | state_NY | state_OH | state_OK | state_OR | state_PA | state_RI | state_SC | state_SD | state_TN | state_TX | state_UT | state_VA | state_VT | state_WA | state_WI | state_WV | state_WY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1.600000e+04 | 1.600000e+04 | 16000.000000 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 16000.000000 | 16000.000000 | 1.600000e+04 | 16000.000000 | 16000.000000 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 16000.000000 | 16000.000000 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 16000.000000 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 1.600000e+04 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.0 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.00000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 | 16000.000000 |
| mean | 1.667694e-16 | -8.243406e-17 | 0.261000 | -2.559480e-16 | -8.903989e-17 | 2.926548e-16 | -2.555595e-17 | -4.130030e-17 | 4.838838e-17 | -3.533851e-14 | -2.584443e-16 | 3.158099e-16 | -4.493956e-15 | 693.165000 | 697.165125 | 9.364731e-17 | 0.525750 | 0.210687 | -2.892131e-17 | -5.148312e-16 | -3.172809e-17 | -1.241507e-16 | -2.401378e-17 | 0.739062 | 0.297938 | 7.833734e-16 | -2.640110e-16 | -9.863187e-16 | 5.351553e-16 | -3.720635e-17 | 1.578598e-16 | 1.020677e-16 | 0.972625 | -7.804174e-17 | 8.437695e-18 | 1.402697e-16 | 8.307331e-17 | 3.383210e-17 | -6.251943e-17 | -5.439260e-16 | -6.276507e-16 | 6.811218e-17 | -8.104975e-17 | 2.676678e-17 | 6.170064e-17 | -9.912210e-17 | 4.843348e-17 | 3.394441e-15 | 7.420106e-17 | 0.987875 | 0.251688 | 0.922937 | 0.359812 | -4.464901e-16 | 3.790787e-16 | 7.372228e-17 | -4.350340e-16 | 1.106060e-17 | 2.710939e-16 | 1.001560e-16 | 1.829890e-16 | -1.922783e-15 | 6.281260e-17 | -2.092170e-15 | 3.892719e-17 | -9.751215e-16 | -9.632573e-17 | -9.729093e-16 | 9.517283e-16 | 1.691425e-15 | 7.864820e-16 | -1.955325e-15 | 6.443058e-16 | 0.464062 | 0.000063 | 0.121250 | 0.413562 | 0.414563 | 0.316000 | 0.195500 | 0.577625 | 0.072938 | 0.004938 | 0.023875 | 0.014875 | 0.009062 | 0.069125 | 0.000812 | 0.013375 | 0.008563 | 0.0 | 0.011875 | 0.008937 | 0.024438 | 0.143750 | 0.020438 | 0.014063 | 0.002063 | 0.003375 | 0.078375 | 0.032188 | 0.005687 | 0.003563 | 0.035937 | 0.018062 | 0.008125 | 0.009500 | 0.011750 | 0.023187 | 0.023438 | 0.002875 | 0.024750 | 0.018500 | 0.014313 | 0.005750 | 0.00275 | 0.030187 | 0.002500 | 0.005188 | 0.003812 | 0.034125 | 0.005625 | 0.015187 | 0.079250 | 0.033188 | 0.008563 | 0.009938 | 0.033188 | 0.004500 | 0.010812 | 0.002000 | 0.017063 | 0.085125 | 0.007063 | 0.025437 | 0.001813 | 0.019125 | 0.013062 | 0.001250 | 0.001500 |
| std | 1.000031e+00 | 1.000031e+00 | 0.439194 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 31.093148 | 31.093760 | 1.000031e+00 | 0.499352 | 0.407810 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 0.439160 | 0.457366 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 0.163179 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 0.109447 | 0.433996 | 0.266699 | 0.479960 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 1.000031e+00 | 0.498722 | 0.007906 | 0.326428 | 0.492487 | 0.492662 | 0.464927 | 0.396598 | 0.493953 | 0.260042 | 0.070096 | 0.152664 | 0.121056 | 0.094768 | 0.253675 | 0.028494 | 0.114878 | 0.092140 | 0.0 | 0.108327 | 0.094118 | 0.154408 | 0.350847 | 0.141496 | 0.117752 | 0.045369 | 0.057998 | 0.268769 | 0.176503 | 0.075203 | 0.059582 | 0.186140 | 0.133182 | 0.089775 | 0.097007 | 0.107762 | 0.150503 | 0.151293 | 0.053544 | 0.155367 | 0.134755 | 0.118779 | 0.075613 | 0.05237 | 0.171108 | 0.049939 | 0.071839 | 0.061630 | 0.181556 | 0.074791 | 0.122302 | 0.270137 | 0.179132 | 0.092140 | 0.099194 | 0.179132 | 0.066933 | 0.103423 | 0.044678 | 0.129508 | 0.279076 | 0.083744 | 0.157455 | 0.042536 | 0.136969 | 0.113546 | 0.035334 | 0.038702 |
| min | -5.668754e+00 | -1.506594e+00 | 0.000000 | -1.660629e+00 | -1.497147e+00 | -1.517425e+00 | -1.814905e+00 | -1.432943e+00 | -9.979486e-01 | -6.778308e-01 | -1.909580e+00 | -3.763290e-01 | -5.651713e+00 | 660.000000 | 664.000000 | -7.091658e-01 | 0.000000 | 0.000000 | -1.898702e+00 | -3.977398e-01 | -7.186158e-01 | -1.999024e+00 | -1.879410e+00 | 0.000000 | 0.000000 | -1.708697e-01 | -1.754900e-01 | -7.584839e-02 | -8.890935e-01 | -9.382181e-01 | -8.193343e-01 | -1.056480e+00 | 0.000000 | -8.641200e-01 | -2.008902e+00 | -9.205974e-01 | -1.141842e+00 | -1.047570e+00 | -2.921467e+00 | -7.002819e-01 | -5.711767e-01 | -9.439492e-01 | -1.522478e+00 | -8.340071e-01 | -6.903186e-01 | -1.913551e+00 | -8.331548e-02 | -2.507871e-02 | -8.131511e-01 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -3.939124e-01 | -1.566318e+00 | -1.561550e+00 | -1.638997e+00 | -1.183329e+00 | -1.757034e+00 | -1.489415e+00 | -1.679084e+00 | -2.500782e-02 | -6.425621e-02 | -1.868231e-01 | -1.214740e+00 | -8.624141e+00 | -1.137415e+00 | -4.018758e-01 | -1.453849e-01 | -1.068134e-01 | -9.320399e-02 | -1.259621e-01 | -9.015560e-02 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | -8.741340e-01 | -7.444671e-01 | 0.000000 | -6.837144e-01 | -7.229014e-01 | -7.716013e-01 | -7.704628e-01 | -9.128776e-01 | -4.012448e-01 | -6.778308e-01 | -6.748075e-01 | -3.763290e-01 | -5.357199e-01 | 670.000000 | 674.000000 | -7.091658e-01 | 0.000000 | 0.000000 | -6.755860e-01 | -3.977398e-01 | -4.622053e-01 | -7.626065e-01 | -7.198970e-01 | 0.000000 | 0.000000 | -1.708697e-01 | -1.754900e-01 | -7.584839e-02 | -8.890935e-01 | -6.092063e-01 | -8.193343e-01 | -4.801920e-01 | 1.000000 | -6.133488e-01 | -4.374109e-01 | -9.205974e-01 | -7.865363e-01 | -6.240137e-01 | -6.468691e-01 | -7.002819e-01 | -5.711767e-01 | -5.623436e-01 | -6.644114e-01 | -6.366822e-01 | -5.828766e-01 | -7.887079e-01 | -8.331548e-02 | -2.507871e-02 | -8.131511e-01 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | -3.939124e-01 | -6.987472e-01 | -5.774057e-01 | -7.815426e-01 | -6.480487e-01 | -7.034194e-01 | -7.538447e-01 | -7.730932e-01 | -2.500782e-02 | -6.425621e-02 | -1.868231e-01 | -7.239486e-01 | -3.181981e-01 | -1.137415e+00 | -4.018758e-01 | -1.453849e-01 | -1.068134e-01 | -9.320399e-02 | -1.259621e-01 | -9.015560e-02 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | -1.056832e-01 | -2.640553e-01 | 0.000000 | -1.727680e-01 | -2.662447e-01 | -2.577753e-02 | -2.443249e-02 | -1.327792e-01 | -1.547802e-01 | -6.778308e-01 | -6.776001e-02 | -3.763290e-01 | 2.513560e-01 | 685.000000 | 689.000000 | -7.091658e-01 | 1.000000 | 0.000000 | -1.513934e-01 | -3.977398e-01 | -2.393893e-01 | -1.264820e-02 | -1.401406e-01 | 1.000000 | 0.000000 | -1.708697e-01 | -1.754900e-01 | -7.584839e-02 | -9.044675e-02 | -2.801946e-01 | 1.602180e-01 | -4.801920e-01 | 1.000000 | -2.602490e-01 | 2.854752e-01 | -3.053276e-01 | -7.592443e-02 | -2.616205e-01 | 7.906649e-02 | -9.482905e-02 | -5.711767e-01 | -1.807380e-01 | -9.236726e-02 | -3.980944e-01 | -3.480124e-01 | 7.446455e-02 | -8.331548e-02 | -2.507871e-02 | -2.600106e-01 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | -3.939124e-01 | -2.649617e-01 | -2.493576e-01 | -1.384520e-01 | -2.465885e-01 | -2.819736e-01 | -1.408694e-01 | -1.690994e-01 | -2.500782e-02 | -6.425621e-02 | -1.868231e-01 | -2.331568e-01 | 3.748806e-01 | -2.190845e-01 | -4.018758e-01 | -1.453849e-01 | -1.068134e-01 | -9.320399e-02 | -1.259621e-01 | -9.015560e-02 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 5.962913e-01 | 5.620352e-01 | 1.000000 | 6.188390e-01 | 5.424971e-01 | 7.200462e-01 | 5.723918e-01 | 1.167385e+00 | 1.695154e-01 | 1.475295e+00 | 6.091988e-01 | -3.763290e-01 | 6.448939e-01 | 710.000000 | 714.000000 | 3.895708e-01 | 1.000000 | 0.000000 | 5.475301e-01 | -3.977398e-01 | 1.338768e-01 | 7.494716e-01 | 5.224382e-01 | 1.000000 | 1.000000 | -1.708697e-01 | -1.754900e-01 | -7.584839e-02 | 7.082000e-01 | 4.881711e-02 | 1.602180e-01 | 6.723841e-01 | 1.000000 | 2.593788e-01 | 7.254928e-01 | 3.099421e-01 | 2.793815e-01 | 3.093317e-01 | 7.082107e-01 | 5.106238e-01 | 1.278043e-01 | 2.008677e-01 | 4.796769e-01 | 3.257373e-01 | 1.628211e-01 | 8.730690e-01 | -8.331548e-02 | -2.507871e-02 | 2.931299e-01 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 2.999771e-01 | 6.026095e-01 | 4.067387e-01 | 5.046385e-01 | 4.225117e-01 | 5.609180e-01 | 4.721059e-01 | 4.348944e-01 | -2.500782e-02 | -6.425621e-02 | -1.868231e-01 | 2.576350e-01 | 6.829156e-01 | 7.020041e-01 | -4.018758e-01 | -1.453849e-01 | -1.068134e-01 | -9.320399e-02 | -1.259621e-01 | -9.015560e-02 | 1.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| max | 2.307412e+00 | 2.739539e+00 | 1.000000 | 2.957678e+00 | 3.759527e+00 | 2.957517e+00 | 3.258101e+00 | 1.167385e+00 | 8.582273e+01 | 1.475295e+00 | 2.881401e+01 | 1.663589e+01 | 1.825508e+00 | 845.000000 | 850.000000 | 4.784517e+00 | 1.000000 | 1.000000 | 1.033246e+01 | 4.094505e+01 | 2.683657e+01 | 4.337110e+00 | 5.905890e+00 | 1.000000 | 1.000000 | 1.960582e+01 | 1.087271e+01 | 4.250570e+01 | 1.029196e+01 | 1.090620e+01 | 9.955741e+00 | 1.046928e+01 | 1.000000 | 1.494211e+01 | 3.554178e+00 | 1.138480e+01 | 1.484692e+01 | 1.846216e+01 | 4.483076e+00 | 1.261968e+01 | 1.620437e+01 | 1.012261e+01 | 1.249260e+01 | 1.268213e+01 | 1.489108e+01 | 4.281581e+00 | 3.694579e+01 | 6.607522e+01 | 8.590238e+00 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 2.319833e+01 | 8.410750e+00 | 1.024818e+01 | 6.506817e+00 | 9.522276e+00 | 1.109706e+01 | 6.847049e+00 | 8.890807e+00 | 3.998750e+01 | 2.810285e+01 | 2.776801e+01 | 1.007347e+01 | 6.829156e-01 | 1.620335e+00 | 1.178541e+01 | 5.779400e+01 | 3.432827e+01 | 2.617172e+01 | 1.225695e+01 | 1.109193e+01 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.0 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
We can now start to explore the data and check for "significant" predictors. First we create training and test sets and then we fit some models to see what important predictors would be according to those models. We also need to encode loan_status first, so LassoCV and DecisionTree won't throw an Exception.
y_train = y_train.replace({'Fully Paid': 1, 'Charged Off': 0})
y_test = y_test.replace({'Fully Paid': 1, 'Charged Off': 0})
alphas = (.1,.5,1,5,10,50,100)
fitted_lasso = LassoCV(alphas=alphas, max_iter=100000).fit(X_train, y_train)
print("Relevant variables according to variable selection via Lasso:\n")
result = {}
for index, val in enumerate(fitted_lasso.coef_):
if fitted_lasso.coef_[index] != 0:
result[fitted_lasso.coef_[index]] = X_train.columns[index]
for key in sorted(result.keys(), reverse=True) :
print(key , ":" , result[key])
Relevant variables according to variable selection via Lasso: 0.0025568715312380545 : fico_range_low 9.484037990805908e-08 : fico_range_high -0.027056759401023214 : sub_grade
Run another check using a Decision Tree and Random Forest and their feature importance to check which variables are important.
dec_tree = DecisionTreeClassifier(max_depth = 10).fit(X_train, y_train)
dec_feat_imp = dec_tree.feature_importances_
dec_feat_imp = 100.0 * (dec_feat_imp / dec_feat_imp.max())
index_sorted = np.argsort(dec_feat_imp)
print("Relevant variables according to variable selection via feature importance using Decision Tree:\n")
result = {}
for index in index_sorted:
if dec_feat_imp[index] != 0:
result[dec_feat_imp[index]] = X_train.columns[index]
for key in sorted(result.keys(), reverse=True) :
print(key , ":" , result[key])
Relevant variables according to variable selection via feature importance using Decision Tree: 100.0 : int_rate 21.304675503227674 : sub_grade 19.197629186871147 : id 17.45445447222068 : dti 15.365088091616434 : avg_cur_bal 10.54705107784583 : revol_util 9.407515079333443 : annual_inc 8.783968761862633 : revol_bal 7.867514221028705 : loan_amnt 7.815662565909309 : bc_util 7.650440076626011 : mort_acc 7.609002056702982 : bc_open_to_buy 7.248263308732877 : installment 7.150234827997563 : num_op_rev_tl 6.7924055844896065 : max_bal_bc 6.395924569186223 : all_util 6.060403629983622 : emp_length 5.419391214879219 : total_bal_il 5.268225971175418 : open_acc 5.181095404879697 : num_rev_accts 4.892252407029087 : pct_tl_nvr_dlq 4.778186645963515 : acc_open_past_24mths 4.573886623238064 : percent_bc_gt_75 4.569155476705875 : earliest_cr_line 4.1197348447983515 : total_acc 4.118571405752972 : fico_range_low 3.883882601325824 : inq_fi 3.756030995318789 : num_actv_bc_tl 3.7184522097559527 : inq_last_12m 3.628998949035904 : fico_range_high 3.5083109537011423 : num_il_tl 3.282716799295942 : num_rev_tl_bal_gt_0 3.107980508341097 : open_il_24m 2.8951557435873747 : il_util 2.4852270134309595 : total_cu_tl 2.339773456316361 : num_bc_tl 2.3374372869119626 : home_RENT 2.309255882223218 : open_rv_24m 2.208079001367195 : num_tl_op_past_12m 2.0823173882060364 : mths_since_last_major_derog 1.9665788867124323 : num_bc_sats 1.8640989718936734 : open_act_il 1.861186957975004 : pub_rec 1.5372791893554898 : num_accts_ever_120_pd 1.4901598576319195 : state_NY 1.311623471612176 : home_OWN 1.0382387766755317 : pub_rec_bankruptcies 0.9579702494687025 : open_rv_12m 0.9122552493326146 : inq_last_6mths 0.8179578873795464 : state_TX 0.7789286312742919 : term 0.7764788426115419 : dti_joint 0.761403310712108 : open_acc_6m 0.7390311567904804 : tax_liens 0.7354112311201012 : state_PA 0.7190819637537914 : state_AZ 0.6455450118912908 : purp_other 0.633641018633876 : purp_home_improvement 0.6138191367547075 : state_NC 0.6090370662071478 : verify_Source Verified 0.6015087982320213 : mths_since_recent_inq 0.5997945975663626 : purp_debt_consolidation 0.5992010829529387 : open_il_12m 0.5739083027571913 : sec_app_revol_util 0.5661390062904715 : num_tl_90g_dpd_24m 0.5581995917558585 : annual_inc_joint 0.5544369655044484 : state_MN 0.5512910649082389 : state_GA 0.5303397827403792 : state_MD 0.5247161625208605 : verify_Verified 0.5032991762955196 : state_FL 0.49337843178168 : state_WA 0.4899693333063094 : state_IN 0.4868810157633434 : state_NH 0.39338839281963617 : state_LA 0.36730131376460196 : home_MORTGAGE 0.26255697131529426 : purp_credit_card 0.25354964380773254 : delinq_amnt 0.2490960244055073 : purp_major_purchase 0.23753598209355994 : delinq_2yrs 0.22062389999429233 : state_CT
# the accuracy score for Decision Tree
accuracy_score(dec_tree.predict(X_test), y_test)
0.608
# Check by running a basic random forest (without tuning parameters)
rf = RandomForestClassifier(n_estimators=int(X_train.shape[1]/2),
max_depth = 2).fit(X_train, y_train)
print("The precision score for the basic RF is:")
precision_score(rf.predict(X_test), y_test, pos_label = 1)
The precision score for the basic RF is:
0.5594132029339853
# Check that the top features make sense
rf_feat_imp = rf.feature_importances_
rf_feat_imp = 100.0 * (rf_feat_imp / rf_feat_imp.max())
sorted_idx = np.argsort(rf_feat_imp)
pos = np.arange(sorted_idx.shape[0]) + .5
#Plot
plt.figure(figsize=(10,24))
plt.barh(pos, rf_feat_imp[sorted_idx], align='center')
plt.yticks(pos, X_train.columns[sorted_idx])
plt.xlabel('Relative Importance')
plt.title('Variable Importance');
Summary:
From what the Lasso and the decision tree evaluation returned, below are the most interesting/significant variables. Those are the top 41, ranked by whether they appeared as important in Lasso, Decision tree and Random Forest. Since sub_grade is a more detailed view of grade, we've only included sub_grade.
int_ratesub_gradegrade, since sub_grade correlates with grade and is more granularavg_cur_baldtibc_open_to_buyfico_range_lowfico_range_high as it correlates with the low range and doesn't provide additional information.installmentemp_lengthtermpercent_bc_gt_75verification_statusrevol_utilall_utilhome_ownershipopen_rv_24mnum_rev_tl_bal_gt_0loan_amntannual_incmort_accmax_bal_bctotal_bal_ilpct_tl_nvr_dlqearliest_cr_linebc_utilrevol_baltotal_accnum_il_tlacc_open_past_24mthsil_utildelinq_amntnum_actv_bc_tlnum_bc_satsopen_rv_12mnum_rev_acctsopen_accopen_acc_6mopen_act_ilnum_op_rev_tltotal_cu_tlinq_fiinq_last_6mths# create dataframe with relevant predictors plus the response variable
eda_predictors = ['int_rate', 'sub_grade', 'avg_cur_bal', 'dti', 'bc_open_to_buy', 'fico_range_low', 'installment',
'emp_length', 'term', 'percent_bc_gt_75', 'verify_Verified', 'revol_util', 'all_util',
'home_MORTGAGE','home_RENT', 'open_rv_24m', 'num_rev_tl_bal_gt_0',
'loan_amnt', 'annual_inc', 'mort_acc', 'max_bal_bc', 'total_bal_il', 'pct_tl_nvr_dlq',
'earliest_cr_line', 'bc_util', 'revol_bal', 'total_acc', 'num_il_tl', 'acc_open_past_24mths',
'il_util', 'delinq_amnt', 'num_actv_bc_tl', 'num_bc_sats', 'open_rv_12m', 'num_rev_accts',
'open_acc','open_acc_6m', 'open_act_il', 'num_op_rev_tl',
'total_cu_tl', 'inq_fi', 'inq_last_6mths', 'loan_status']
# this reduced dataframe is created to be able to run a pairplot analysis
df_eda = df[eda_predictors]
df_eda.shape
(20000, 43)
sns.pairplot(df_eda.sample(1000), hue='loan_status');
CS109A Introduction to Data Science: